A large-scale sentiment analysis for Yahoo! answers

Abstract

Sentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not investigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user's ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in advertising, recommendation, and search.

How can i improve my app? Classifying user reviews for software maintenance and evolution

“…Sentiment analysis is the process of assigning a quantitative value to a piece of text expressing an affect or mood [27]. We consider sentiment analysis as a text classification task which assigns each given sentence in a user review to one corresponding class.…”

Interactive use of online health resources: a comparison of consumer and professional questions

“…We also suspect some communities might be better suited for younger audiences (eg, Yahoo! Answers 34 ).…”

Language, demographics, emotions, and the structure of online social networks

“…2 To each tweet in our dataset, quantifying positive sentiment P 2 ½þ1; þ5] and negative sentiment N 2 ½À1; À5], consistently with the Positive and Negative Affect Schedule (PANAS) [42]. SentiStrength has been shown to perform very closely to human raters in validity tests [41] and has been applied to measure emotions in product reviews [43], online chatrooms [44], Yahoo answers [45], Youtube comments [46], and social media discussions [47]. In addition, SentiStrength allows our approach to be applied in the future to other languages, like Spanish [30,48], and to include contextual factors [49], like sarcasm [50].…”