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Gambling Sentiments - Views on Gambling, Gambling Sentiments Analysed in Canada

Gambling Sentiments Analysed in Canada

In the United States, feelings about gambling tend to play along socioeconomic lines: Areas with high levels of college-educated or high-income individuals tend to be more tolerant of gambling, while more rural and more religious areas tend to be more critical. However, as 13 states do not have any form of casino-based gaming, there are large pockets of the nation where there is no Instagram-related sentiment about gambling – positive or negative. Looking at the actual hashtags that carried the highest sentiment score has yielded interesting results. For example, the two gambling hashtag categories that yielded the most positive average sentiment scores were “bingo” and “slots.” 51 percent of millennials and 61 percent of all casino-goers favour slots and video poker as their casino games of choice.

Gambling Sentiments - Views on Gambling

The question of whether or not millennials gamble is an open-ended one. Pew Research found that state casino gambling revenue is down; the bottom line: The younger generation is shunning the same games of chance that captivated their parents and grandparents. Millennials demand fair gaming and seek interactive experiences that allow them to use skill and finesse. And casinos may need to adapt to best suit these social techies.

60 percent of all Instagram users are between the age of 18 and 34, so examining gambling trends on Instagram may grant insight into the thoughts of millennials. Using a sentiment analysis tool – which identifies positive and negative statements, feelings, and opinions within text – we examined gambling-related hashtags on Instagram. Tracking the prevalence of the positive and negative words people used helped us determine the general feelings behind gambling posts and to get an idea of millennial sentiment toward this brand of gaming. Keep reading to see what we discovered.

Categorizing Gambling Love on Instagram

The above graph reflects the average sentiment score of the Instagram posts we analyzed. A score between zero and one is positive, while a score between zero and negative one is regarded as a negative score. In our analysis of the Instagram gambling hashtags, we found that – overwhelmingly – Instagram users thought positively of gambling. We found 33,926 Instagram hashtags that were construed as positive, compared to 9,636 that were negative, and 8,309 that were neutral. (Note: We could not determine the sentiment of 4,776 posts.)

Looking at the actual hashtags that carried the highest sentiment score has yielded interesting results. For example, the two gambling hashtag categories that yielded the most positive average sentiment scores were “bingo” and “slots.” 51 percent of millennials and 61 percent of all casino-goers favour slots and video poker as their casino games of choice.

Other categories that yielded high sentiment scores were “World Poker Tour” and “Heartland Poker Tour”.

Worldwide Feelings on Gambling

The highest sentiment scores were found in areas where gambling constitutes a major portion of the culture or economy. See Catalonia, for example – a Spanish dependency with an instant lottery and casinos that form a large part of the region's GDP. One study suggests that 90.7 percent of its population have gambled. Atlantic City and Northwestern Russia also produce some of the highest sentiment scores toward gambling.

The countries whose population has the highest moral opinion on gambling – France, Canada, the United States, Great Britain, Spain, Japan, and Germany – also have the most positive gambling sentiment score among Instagram posts. Negative sentiment scores can be found in countries with citizens who believe that gambling is immoral, such as Chile and Pakistan.

Overwhelmingly, the sentiment toward gambling on Instagram seems to follow the social acceptance of gambling in the posts' originating countries.

Feelings on Gambling in the U.S.

Conclusion

Millennials still love to gamble and typically feel good about it, but our examination proves it's a more social experience for them. Slots are still king, but gambling games with a social edge, such as bingo and poker, are becoming the focus. Gambling has changed from something that's luck-focused to a game that requires skill and social interaction. Still the same, social expectations and norms about gambling colour opinions about gambling online.

Comedian Jerry Lewis once said that gambling is part of the human condition. If this is true, then America's love of gaming is unlikely to die anytime soon. How we gamble, however, will change, just as the faces of those gambling will change.

Sources

https://www.pewtrusts.org/en/research-and-analysis/blogs/stateline/2015/09/15/state-gambling-revenue-takes-hit-as-millennials-bring-new-habits-to-casinos

https://www.gettoknowgaming.org/

https://www.statista.com/statistics/398166/us-instagram-user-age-distribution/

https://www.fool.com/investing/general/2015/09/19/the-millennial-problem-why-we-dont-gamble.aspx

https://www.washingtonpost.com/posteverything/wp/2015/06/10/heres-how-my-graduating-class-ended-up-with-72-valedictorians/?hpid=z11

https://unhyphenatedamerica.org/2015/06/10/fairness-and-millennials-they-dont-know-that-they-dont-know/

https://www.insightsassociation.org/article/your-casino-optimized-millennials

https://www.cnbc.com/2015/09/30/gambling-industry-tries-to-figure-out-millennials-.html

http://thegbrief.com/articles/can-casinos-gamble-on-millennials-and-get-it-right-612

https://www.entrepreneur.com/article/245920

https://www.washingtonpost.com/news/the-switch/wp/2015/02/11/vegass-new-bet-on-grabbing-millennials-turning-slots-into-video-games/

http://www.pewglobal.org/2014/04/15/global-morality/table/gambling/

https://www.people-press.org/2014/06/26/section-8-health-care-marijuana-common-core-other-domestic-issues/pp-2014-06-26-typology-8-06/

https://fortune.com/2015/07/02/instagram-fraud-spam/

https://www.businessinsider.com.au/chaos-ensues-as-instagram-deletes-millions-of-accounts-2014-12

https://www.theatlantic.com/business/archive/2014/08/a-good-way-to-wreck-a-local-economy-build-casinos/375691/

http://americanvalues.org/catalog/pdfs/why-casinos-matter.pdf

Methodology

We scraped Instagram for each of the following hashtags related to gambling tournaments: #worldpokertour, #WPTchina, #WPT5D, #WPTalpha8, #WPTmontreal, #WPTfoundation, #WPTprague, #WPTcruise, #WPTchoctaw, #WPTjax, #WPTlegends, #WPTlegendsofpoker, #EPTbarcelona, #EPTmalta, #EPTendirecto, #EPTlive, #hptpoker, #hptpokertour, #hptstl, #hptvb, #worldseriesofpoker, #wsop, #wsop2015, #wsopmainevent, #wsopc, #wsopcircuit, #wsop15, #wsopme, #wsope, #wsopeurope, #wsopapac, #aussiemillions, #2015acop, #acop, #crownpoker, #lapt, #LAPC, #LApokerclassic, #blackjacktournament, #roulettemasters, #slotstournament, #tournevent, #tourneventofchampions, #milliondollarevent, #baccarattournament, and #bingotournament. We then ran the caption of each post through AlchemyAPI to evaluate sentiment and categorized and visualized the results.

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Sentiment Bias and Asset Prices: Evidence from Sports Betting Markets and Social Media

Author

(University of Southern Denmark, Department of Environmental and Business Economics)

(West Virginia University, College of Business and Economics)

(Louisiana State University, School of Kinesiology)

Abstract

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Other versions of this item:

Arne Feddersen & Brad R. Humphreys & Brian P. Soebbing, 2017. " Sentiment Bias And Asset Prices: Evidence From Sports Betting Markets And Social Media ," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 1119-1129, April.

References listed on IDEAS

Arne Feddersen & Brad Humphreys & Brian Soebbing, 2013. " Sentiment Bias in National Basketball Association Betting ," Working Papers 13-03, Department of Economics, West Virginia University.

Egon Franck & Erwin Verbeek & Stephan Nuesch, 2008. " Sentimental Preferences and the Organizational Regime of Betting Markets ," Working Papers 0089, University of Zurich, Institute for Strategy and Business Economics (ISU), revised 2010.

Charles Lee & Andrei Shleifer & Richard Thaler, 1990. " Investor Sentiment and the Closed-End Fund Puzzle ," NBER Working Papers 3465, National Bureau of Economic Research, Inc.

Lee, Charles & Shleifer, Andrei & Thaler, Richard H., 1991. " Investor Sentiment and the Closed-End Fund Puzzle ," Scholarly Articles 27693394, Harvard University Department of Economics.

Citations

Fischer, Kai & Haucap, Justus, 2020. " Betting market efficiency in the presence of unfamiliar shocks: The case of ghost games during the COVID-19 pandemic ," DICE Discussion Papers 349, University of Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).

Arne Feddersen & Brad Humphreys & Brian Soebbing, 2013. " Sentiment Bias in National Basketball Association Betting ," Working Papers 13-03, Department of Economics, West Virginia University.

Rebeggiani, Luca & Gross, Johannes, 2018. " Chance or Ability? The Efficiency of the Football Betting Market Revisited ," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181563, Verein für Socialpolitik / German Economic Association.

Raphael Flepp & Stephan Nüesch & Egon Franck, 2014. " The Liquidity Advantage of the Quote-Driven Market: Evidence from the Betting Industry ," Working Papers 342, University of Zurich, Department of Business Administration (IBW).

Herman O. Stekler & David Sendor & Richard Verlander, 2009. " Issues in Sports Forecasting ," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

Palomino, F.A. & Renneboog, L.D.R. & Zhang, C., 2008. " Information Salience, Investor Sentiment, and Stock Returns : The Case of British Soccer Betting ," Discussion Paper 2008-99, Tilburg University, Center for Economic Research.

Palomino, F.A. & Renneboog, L.D.R. & Zhang, C., 2008. " Information Salience, Investor Sentiment, and Stock Returns : The Case of British Soccer Betting ," Other publications TiSEM 91f34e3c-7702-4ab3-bf1d-7, Tilburg University, School of Economics and Management.

Humphreys, Brad & Paul, Rodney & Weinbach, Andrew, 2010. " Consumption Benefits and Gambling: Evidence From the NCAA Basketball Betting Market ," Working Papers 2010-7, University of Alberta, Department of Economics.

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Keywords

JEL classification:

L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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Affective responses to uncertain real-world outcomes: Sentiment change on Twitter

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America

Roles Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliation Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America

Roles Writing – original draft, Writing – review & editing

Affiliation WMG, University of Warwick, Coventry, West Midlands, United Kingdom

Affective responses to uncertain real-world outcomes: Sentiment change on Twitter

Figures

Abstract

We use data from Twitter.com to study the interplay between affect and expectations about uncertain outcomes. In two studies, we obtained tweets about candidates in the 2014 US Senate elections and tweets about National Football League (NFL) teams in the 2014/2015 NFL season. We chose these events because a) their outcomes are highly uncertain and b) they attract a lot of attention and feature heavily in the communication on social media. We also obtained a priori expectations for the events from political forecasting and sport betting websites. Using this quasi-experimental design, we found that unexpected events are associated with more intense affect than expected events. Moreover, the effect of expectations is larger for outcomes that fall below expectations than outcomes that exceed expectations. Our results are consistent with fundamental principles in psychological science, such as reference-dependence in experienced affect. We discuss how naturally occurring online data can be used to test psychological predictions and develop novel psychological insights.

Citation: Bhatia S, Mellers B, Walasek L (2019) Affective responses to uncertain real-world outcomes: Sentiment change on Twitter. PLoS ONE 14(2): e0212489. https://doi.org/10.1371/journal.pone.0212489

Editor: Huijia Li, Central University of Finanace and Economics, CHINA

Received: October 17, 2017; Accepted: February 1, 2019; Published: February 27, 2019

Copyright: © 2019 Bhatia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All data files are available from the OSF database: https://osf.io/b2yfz/.

Funding: The work was supported by the Directorate for Social, Behavioral and Economic Sciences (SES-1626825) to Dr. Sudeep Bhatia. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

How do we feel when a favored political candidate loses an election or a home team beats last year’s champion? A key determinant of our affective responses is our beliefs about the event, with unexpected events leading to more intense affective responses than expected events [1–3]. When evaluating an event, we compare the outcome of the event to a reference point. Often, the reference point is the expected outcome. When the actual outcome exceeds the expectation, it is evaluated even more positively. When it falls short of the expectation, it is evaluated negatively.

Theoretical frameworks in which affective responses depend on reference points are abundant in psychology, economics, and political science. These frameworks include theories of motivation and causal attribution in social psychology [4,5], reinforcement learning models in cognitive psychology and neuroscience [6–8], theories of preference in judgment and decision-making and behavioral economics [9–13], and aspiration based models in politics [14]. These models share the assumption that evaluative processes involve expectations about outcomes, and they often assume that the psychological impact of losses and gains is not the same (also see [15]). Taken together, a shared principle that we focus on in the present paper is that expectations influence affect, and the interaction between outcomes and beliefs influences emotional reactions to a wide range of human behavior.

Although the psychological principles of reference-dependence have been extensively studied, much of the existing evidence is confined to the controlled laboratory settings, where outcomes are artificial or hypothetical. This is understandable. It is often difficult to quantify the evaluations of outcomes in real-world settings. Additionally, the precision necessary to study the interactions between expectation and affect can best be achieved by experimental manipulation. A dataset that captures these two variables in a naturalistic setting is, however, very valuable. First, large volumes of unsolicited data are not constrained by the features of the experimental designs, and may therefore provide more conservative tests of existing theories. Second, naturalistic data can offer novel insights when it includes information that is not easily manipulated/measured in the experimental setting (e.g., time course, geographical location). Consequently, if the dataset is rich enough in contextual information, it can be used to examine variables that moderate the predictions of theories.

In the following paper, we present a novel approach to study the interplay between affect and expectations in a naturalistic setting. We present the results of two studies analyzing millions of online posts (tweets) from the social networking site, Twitter. In particular, we obtained tweets about candidates in the 2014 US Senate elections and tweets about National Football League (NFL) teams in the 2014/2015 NFL season. We also obtained a priori expectations for these elections and games using predictions of political forecasters and point spreads offered by popular betting websites. Our goal was to examine whether the affective content of the tweets was influenced by the expected outcomes of the elections and games. More specifically, we assessed how tweets’ affect (as measured by their sentiment score) changed when the results of Senate elections and NFL matches became known. Our prediction was that the expectations people held about these outcomes would influence the strength of sentiment change. Both surprising losses and surprising wins should produce stronger affective responses than losses and wins that were unsurprising. As such, we set out to determine whether the results of our quasi-experiments adhere to affective and cognitive theories of psychology.

Study 1: United States Senate elections

Materials and methods

Study 1 examined tweet affect in the United States Senate elections held on Tuesday, November 4, 2014. We chose United States Senate election for two reasons. First, Senate elections are prolific enough to feature heavily in the communication on social media. We were therefore able to obtain large volumes of tweets relating to elections in different geographical regions (US States). Second, while outcomes of the elections are highly uncertain, it is possible to obtain predictions for them from various forecasting sites. On the day before the elections, we obtained predictions from five popular political forecasting newsletters and blogs: The Cook Political Report, Sabato’s Crystal Ball, The Rothenberg Political Report, Real Clear Politics, and Daily Kos Elections. Predictions for each of these blogs were made on a scale with labels of Safe Democrat, Likely Democrat, Lean Democrat, Tossup/Tilt Democrat, Tossup, Tossup/Tilt Republican, Lean Republican, Likely Republican, and Safe Republican. We averaged predictions over blogs to generate an aggregate expectation for each election, ranging from -4 (Safe Republican) to +4 (Safe Democrat). We then recoded predictions into a variable that captured the expected outcome for each candidate. This variable, based on whether the winning candidate would be a Democrat or Republican, ranged from -4 (very likely to lose) to +4 (very likely to win).

We also obtained 372,981 tweets about the candidates in these elections, posted in a two-week period between October 31 st and November 14 th . Using Twitter’s data streaming feature (official Twitter API), we collected all tweets that mentioned the full names of the Democratic and Republican candidates for 19 Senate seats that were not considered safely Democrat or safely Republican by all five of our blogs (i.e. seats with expected outcomes that were strictly greater than -4 and strictly less than +4). We excluded these cases as we were mainly interested in events that were most uncertain–i.e. where the expectations are most likely to not match reality. We removed tweets mentioning two or more candidates and removed tweets about the Louisiana and Alaska elections because Louisiana has a “jungle” primary system in which runoff elections are held if no candidate receives a majority of votes. Indeed, in the 2014 elections this was thee case, and the winner was not determined until December, 2014. The 2014 election in Alaska was exceptionally tight, and final results were not confirmed until November 17, 2014. All other Senate races were resolved by the end of Election Day on November 4, 2014. Note that our study is purely observational, in compliance with Twitter user agreements, and the tweets were analyzed without any threat to personal privacy.

To calculate tweet affect we used the Sentiment140 lexicon [16], combined with pointwise mutual information methods (PMI) [17]. This popular lexicon is based on a corpus of 1.6 million preexisting tweets with positively and negatively valenced emoticons. PMI methods assign to each of the 62,468 unique words in the lexicon an affective rating between -5 and +5 based on their co-occurrence with positively and negatively valenced emoticons. Words that commonly occur with positive emoticons, but not with negative emoticons, are considered high in positive affect. Similarly, words that commonly occur with negative emoticons, but not with positive ones, are considered high in negative affect. More formally, for items x and y (e.g. the word “happy” and the “smiley face” emoticon), the pointwise mutual information of the two items is given by . Here p(x,y) is the probability of the co-occurrence of x and y in the corpus, and p(x) and p(y) are the individual probabilities of occurrence of x and y in the corpus. Controlling for p(x) and p(y), the PMI of x and y will be larger if p(x,y) is larger, that is, if the two items co-occur alongside each other frequently.

To calculate tweet affect, we averaged the affective ratings of the tweets’ component words. Tweets composed entirely of words absent from the Sentiment140 lexicon were excluded from the analysis (these correspond to approximately 0.03% of our dataset). We manually compiled event prediction data for each candidate. Using computational methods, we also compiled twitter data and tweet affect for each candidate. We then matched the two datasets based on the candidate names. This matching was done computationally, using Python, prior to our regression analysis and other related statistical tests.

We were especially interested in tweets posted after 6am EST on November 5 th , after election results had been announced. We chose 6am as it provided a single point of time at which all election results in our analysis had been announced–we do not expect our result to change with minor modifications to the cutoff time, as most of the tweets were created later on in the morning of November 5 th ). There were 101,283 such tweets. Candidate-level variability in post-election tweet affect might be confounded with pre-election expectations; candidates who were expected to win might have systematically higher or lower tweet ratings than candidates expected to lose. We thus standardized affective ratings for tweets after 6am on November 5 th by subtracting the average pre-election affective ratings of tweets for each candidate before 6am on November 4 th (before Election Day). This adjustment gives us the change in tweet affect pre and post-election–with a positive score implying that post-election tweet affect was more positive than average pre-election tweet affect for that candidate. The average change in tweet affect after 6am EST on November 5th was -0.003 (SD = 0.21, min = -1.70, max = 2.55), indicating that tweet affect dropped slightly after election results were announced. Summary statistics for the change in tweet affect, and other relevant variable, are provided in S1 File.

Results

To determine whether change in tweet affect depended on expectations about the candidate’s chances of winning, we created binary scores for less certain and more certain beliefs referred to as weak and strong expectations, respectively. The expected outcome scale ranged from -4 to +4, and our cutoffs were +/- 2 points (corresponding to an expectation of “safe” or “very safe” for the candidate or their opponent). The analysis compared changes in tweet affect for winners who were strongly expected to win (defined as expected outcome ≥ 2) with winners who were weakly expected to win (2 > expected outcome). We also compared changes in tweet affect for losers who were weakly expected to lose (expected outcome > -2) with losers who were strongly expected to lose (-2 ≥ expected outcome). These four categories corresponded to expected wins, surprising wins, surprising losses, and expected losses.

Expectations were far from random; there was a strong correlation between the candidate’s expected outcome and the percentage of votes that was obtained (r = 0.85, p Fig 1. Average change in tweet affect for candidates who won and were strongly or weakly expected to win (expected win and surprising win), and candidates who lost and were strongly or weakly expected to lose (expected loss and surprising loss) in Study 1.

This figure also displays change in tweet affect for all winners and all losers (Combined Win and Combined Loss). Error bars correspond to +/- 1 standard error.

The above analysis provides a convenient overview of the key trends in our data, however it does not accommodate candidate-level characteristics such as party affiliation and incumbency. It also excludes variability in the data by considering only binary measures of expectation (strong vs. weak expectation). Thus, we more rigorously examined the relationship between change in tweet affect (dependent variable) and expectation about the elections’ outcomes (independent variable) in linear regressions. Our regressions controlled for the outcome (whether the candidate won or lost) as well as the candidate’s party affiliation and incumbency. Consistent with Fig 1, there was a negative effect of expected outcome (β = -0.0065, t = -6.69, 95% CI = [-0.0085, -0.0047], p -4.4 and expected score difference ≤ -4.4, respectively). Average changes in tweet affect for these four groups appear in Fig 2. Losing teams that were weakly expected to lose had a more negative change in tweet affect than losing teams that were strongly expected to lose (β = -0.015, t = -22.57, 95% CI = [-0.016, -0.013], Cohen’s d = -0.06, p Fig 2. Average change in tweet affect for teams that won and were strongly or weakly expected to win (expected win and surprising win), and teams that lost and were strongly or weakly expected to lose (expected loss and surprising loss) in Study 2.

This figure also displays change in tweet affect for all winners and all losers (Combined Win and Combined Loss). Error bars correspond to +/- 1 standard error.

The above analysis describes the key trends in our data, however it does not accommodate game-level characteristics such as whether the game was played at the team’s home stadium and whether the game was in the regular or the post season. It also excludes variability in the data by considering only binary measures of expectation (strong vs. weak expectation). We thus conducted a linear regression on our entire dataset, with change in tweet affect as the dependent variable and the (continuous) expected score difference for the associated team as the independent variable. This analysis controlled for game outcome (winning or losing), whether the game was played at the team’s home stadium, and whether the game was in the regular or the post season. With multiple games per team, we also included team-level random intercepts. There was a negative effect of expected score difference (β = -0.0044, t = -96.67, 95% CI = [-0.0045, -0.0043], p Fig 3. The average change in tweet affect for each team in each game plotted against the relative final score difference for the team in the game, in Study 2.

The solid lines show predictions of the discontinuity regression performed on pooled data. As indicated by this regression, there is a discrete jump in change in tweet affect as teams surpass expectations.

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What is Sentiment Analysis? – AI in Social Media

What Is Sentiment Analysis In Social Media

What is Sentiment Analysis? – AI in Social Media

Not so long ago, AI seemed like a concept straight out of a science fiction movie. Nowadays, we see it virtually everywhere. Manufacturing, healthcare, finance, transportation, social media—all of these industries are based on AI to a great extent. Another sector which benefits from AI and machine learning is marketing. AI has a hand in a massive amount of our marketing activity from putting keyword clusters together and utilising chatbots to personalisation and text analysis.

One of such uses of AI and machine learning is sentiment analysis. Innovative marketers use this to their advantage to monitor brand reputation, gather feedback and avoid PR crises. Here’s how sentiment analysis works, including the different types, the benefits, and the best tools.

Defining sentiment analysis

Put merely; sentiment analysis is the practice of defining whether a statement has a negative, positive, or neutral sentiment. More advanced sentiment analysis tools have the power to establish whether the message is emotionally sad, happy, or angry.

AI and machine learning make sentiment analysis possible, as the algorithms can learn by zooming through tons of data. The more data an algorithm analyses, the more accurate the result. To acquire the required information, you need software to conduct social listening. Generally speaking, this is the act of gathering all of the mentions of a particular keyword online.

“In 2021, competition is fierce and, as such, it’s never been more important to keep an eye on what’s being said about your brand and your offering. I use sentiment analysis tools constantly to make sure there’s nothing negative being said about my brand and to nip any issues in the bud,” says Jacek Żmudziński, senior associate at Future Processing

Such monitoring is not only limited to social media. Tools can analyse the mentions containing a specific keyword from all over the internet.

Because all of the views and opinions are given by people are subjective, we can assign a sentiment to them. Based on certain words, we can identify the author’s feeling towards something. Establishing whether a statement was subjective or objective is not that simple. Let me illustrate:

All bachelors are unmarried. -> this sentence is factual, and it is objective.

All unmarried people are lonely. -> this is an opinion, a subjective statement.

Distinguishing between these becomes an entire problem of its own. Subjective statements are based upon one’s feelings, predictions, and experiences—the algorithm needs to know that.

A metric called sentiment score has been established by sentiment analysis professionals to help assess opinions. Sentiment score is pretty straightforward to calculate—it consists of sweeping statements, total negative statements and absolute positive statements. Still, different tools use different methods and algorithms of recognising the positivity and negativity so that the sentiment score may differ from app to app.

Business sentiment turned positive in Canada in fourth-quarter, BoC survey finds

OTTAWA (Reuters) - Business sentiment in Canada has turned slightly positive for the first time since the COVID-19 pandemic began, with the outlook bolstered by stronger foreign and domestic demand, according to a Bank of Canada survey.

The survey, conducted ahead of many tougher restrictions aimed at curbing surging COVID-19 infections, found that while half of businesses say their current sales are below pre-pandemic levels, most expect them to rise in the next 12 months.

"The Business Outlook Survey indicator continued to recover and turned slightly positive, signaling improved business sentiment," the Bank of Canada said in a report on Monday.

"Robust foreign demand, improved confidence related to vaccines, and ongoing government relief programs all contribute to the improved outlook."

The survey of around 100 firms was conducted between Nov. 16 and Dec. 4, prior to a number of Canadian provinces imposing stricter restrictions to contain the rapid spread of COVID-19 and before the vaccine rollout began.

While business sentiment strengthened overall, it remained solidly negative for many firms. Indeed, one-third of businesses - mainly those providing high-contact services - do not expect sales to return to pre-pandemic levels in the next 12 months.

Still, the investment and employment outlook improved from earlier surveys, with about half of firms now expecting to increase their workforce in 2021.

Inflation expectations, meanwhile, eased slightly, with the majority of firms expecting inflation to remain below 2% for the next two years.

The survey also found improved confidence on vaccine development, though most firms questioned did not anticipate positive impacts from vaccines to materialize until later in 2021.

The Canadian dollar was trading 0.9% lower at 1.2803 to the greenback, or 78.11 U.S. cents, as rising coronavirus cases globally weighed on investor sentiment.

(Additional reporting by David Ljunggren and Fergal Smith; Editing by Mark Heinrich)

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Australian Gambling Research Centre

Gambling activity in Australia

Executive Summary

This report provides an overview of gambling activity in Australia in 2015, with respect to participation, expenditure, and problems among regular gamblers. The report follows a format and style common to gambling prevalence studies conducted in Australia and elsewhere.

As with those studies, the report is intended as a reference document. It is written primarily for researchers and government officials who have an interest in Australian gambling statistics. This report makes a unique contribution to knowledge of gambling in Australia, since Australia has no prior history of surveying and reporting on gambling activity among regular gamblers at the national level.

The content consists primarily of descriptive statistics with a focus on population estimates. The statistics were obtained from cross-sectional analysis of Household, Income and Labour Dynamics in Australia (HILDA) Survey data, wave 15, which is the first wave to include gambling questions. The HILDA Survey was designed so that participants' responses (17,606 participants in wave 15) could be generalised to the Australian adult population.

The participation statistics include population-representative estimates of the proportion and number of Australians who spent money on up to ten common gambling activities (lotteries, instant scratch tickets, electronic gaming machines, race betting, sports betting, keno, casino table games, bingo, private betting and poker) in a typical month of 2015. The report refers almost entirely to these gamblers, which we refer to as regular gamblers.

Chapter 1 of this report provides the background to the study and details regarding study design and methodology. Chapters 2 and 3 respectively provide statistics regarding typical gambling participation and expenditure. Chapters 4 and 5 address participation and expenditure among adults who experienced gambling-related problems. In Chapter 6 gambling expenditure is positioned within the household budgets of low, middle and high-income households. As well, rates of financial stress are compared between households that contain members with and without gambling problems. Additional tables, including a comparison of the HILDA Survey gambling statistics with recent state/territory and national prevalence data and industry revenue data, can be found in the Appendices.

The report identifies an estimated 6.8 million regular gamblers in 2015, among whom lottery participation was very common (76%). Instant scratch tickets (22%) and electronic gaming machines (EGMs; 21%) followed, attracting 1.4 to 1.5 million gamblers. Less than a million gambled regularly on anything else, including racing (14%), sports betting (8%), keno (8%), casino table games (3%), bingo (3%), private betting (2%) and poker (2%). It was common for people to participate either solely in lotteries (59%), or a combination of lotteries and up to two additional activities.

While lotteries and instant scratch tickets were the most popular activities, individual gamblers spent comparatively little on these activities in a typical month, and therefore over the entirety of the year ($695 and $248 per year on average). Those who gambled on Electronic Gaming Machines spent a great deal more per year ($1,292 on average). So too did those who regularly gambled on races ($1,308), sports ($1,032), casino table games ($1,369), and particularly poker ($1,758).

Regular gamblers, viewed by activity, have quite different profiles. For example, compared to the Australian population:

lottery participants were over-represented among older couples living without children;

EGM participants were over-represented among people for whom welfare payments formed their main source of income;

bingo participants were over-represented among retired women living alone;

regular race or sports bettors were over-represented among men on higher incomes, yet the race bettors were more likely to be older and live in outer regional/remote areas; and

sports bettors were more likely to be younger and live in an inner-regional area or major city.

Gambling problems are indicated in the HILDA Survey by endorsing one or more items on the Problem Gambling Severity Index (PGSI). According to the standard use of the PGSI, 1.1 million regular gamblers were estimated to have behaved in ways that caused or put them at risk of gambling-related problems.

Among this subset of regular gamblers, there were more sociodemographic similarities than differences. Those who experienced problems were generally more likely to be young, single, unemployed or not employed (excluding retirees and full-time students), Indigenous, men, living in rental accommodation, in a low socioeconomic area, and were more likely to draw their income from welfare payments than those who had no problems.

Those with problems were also more likely to participate regularly in certain activities. This led to rates of problems being particularly high among participants in six activities (EGMs, race betting, sports betting, casino table games, private betting, and poker) with almost 1-in-2 gamblers on any of these activities experiencing one or more issues.

Another thing those with problems had in common was higher than average spending on gambling. This was particularly so among EGM, race and sports betting participants. Those experiencing the greatest problems spent more than four times as much on these activities, and on gambling overall, as those without problems. Well over half of all expenditure by regular gamblers on these activities came from people who had problems.

Overall, more than forty percent of gambling expenditure by regular gamblers, aggregated across all activities, was accounted for by the 17% who experienced problems.

Gambling expenditure has significant financial ramifications for low-income households, particularly among households where gamblers experienced problems. Gamblers living in low-income households spent a much greater proportion of their household's total disposable income on gambling than high-income households (10% vs 1% on average) - this despite spending less in actual dollar terms ($1,662 vs $2,387).

Gamblers who had problems spent much more of their households' income on gambling than other regular gamblers, with those experiencing severe problems in low-income households spending an average 27% of their disposable household income on gambling - equivalent to four times their yearly household utility bills, or more than half the grocery bills for that income group.

Consistent with these patterns of expenditure, the households of those with gambling problems had a much greater proportion of stressful financial events. Inability to pay electricity, gas or telephone bills on time, and needing to ask friends or family for financial help, were common occurrences.

Future waves of the HILDA Survey will provide nationally representative longitudinal data with which to measure changes in gambling activity and effects on individuals and their households.

The authors would like to thank all those colleagues who contributed to creating gambling questions for the HILDA survey and for their input into this report. In particular, we would like to thank:

Doctor Anna Thomas, Australian Gambling Research Centre, Australian Institute of Family Studies

Doctor Jennifer Baxter, Australian Institute of Family Studies

Assistant Professor Nicki Dowling, Deakin University

Professor Bryan Rodgers, Australian National University

Acting Director Rachel Henry, Welfare Quarantining and Gambling Branch, Department of Social Services

Professor Mark Wooden, Melbourne Institute of Applied Economic and Social Research, University of Melbourne

Doctor Diana Warren, Australian Institute of Family Studies

Director Anne Hollonds, Australian Institute of Family Studies

And all of the participants who took part in the HILDA Survey and made this report possible.

Disclaimer

This report uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to either DSS or the Melbourne Institute. As well, the views expressed may not reflect those of the Australian Institute of Family Studies or the Australian Government.

Outreach Unveils Groundbreaking AI-Powered Buyer Sentiment Analysis; Transforming Sales Engagement

At the first "Outreach Explore" event, the company showed a host of new features, including deal acceleration and admin tools, and a certification program

SEATTLE , Dec. 8, 2020 /PRNewswire/ -- Outreach, the number one sales engagement platform, today announced a brand new product, Outreach Insights, featuring Buyer Sentiment Analysis — introducing a first-of-its-kind artificial intelligence capability that captures buyer emotions and signals to more accurately measure sales engagement versus activity metrics like reply, click, and open rates.

"For too long, the sales industry has been stuck relying solely on insufficient activity-based metrics, like reply rates, to measure engagement because it's all we had. As most sellers know, buyers buy on emotion and justify with facts. Now, we have made an important breakthrough with the ability to detect and classify emotive buyer signals to help sellers more successfully engage customers," said Manny Medina , CEO of Outreach. "Over the past three years, we've been doubling down on the hard work and investments required to enable our world-class team of data scientists to train our machine learning models to detect and classify buyer sentiment based on billions of sales engagement transactions, enabling transformative new value for our customers."

Outreach Insights uses Buyer Sentiment Analysis to help sales leaders and their teams optimize Sequence and content performance in real-time based on emotive buyer signals. It also provides in-the-moment diagnostic and coaching tools so reps can be coached and brought back on pace to accelerate revenue goals. Reports can drill into how prospects were Sequenced, prospect meeting conversion rates, touches per prospect, and response times.

"Our research, based on a study with hundreds of private beta customers, found that by using only the reply rate to measure performance, 40% of sales leaders will mistake a lower-performing Sequence for a higher one. Without sentiment analysis capabilities, even the most seasoned sales leaders were surprised to learn they were wrong nearly half the time. Imagine being able to always pick the content that has the best chance to close a deal. Now, it's a reality. We enable outcomes over output," added Medina.

Outreach Insights, featuring Buyer Sentiment Analysis, will be available in public beta to all Outreach customers at the beginning of 2021 and generally available later in 2021.

Outreach also unveiled a host of additional capabilities that are relevant for roles across the revenue organization. New features include Deal Acceleration tools that enable sales leaders to easily see a team's or individual rep's pipeline, top accounts, and engaged prospects, as well as which deals are moving or are at-risk. This information makes it easy for sales leaders to see how a deal is being worked and provide reps with guidance and in-the-moment coaching. The company also launched a new, industry-leading certification program for administrators and enterprise-grade admin tools that have delivered time savings of up to 20 hours per month for some of Outreach's largest customers.

So, let's define, what was the most valuable conclusion of this review: Gambling Sentiment Analysis - We analysed 1000s of social media posts to see whether people felt positive or negative about gambling topics. at Gambling Sentiments Analysed in Canada