TSentiment: On gamifying Twitter sentiment analysis

Abstract

Social media platforms contain interesting information that can be used to directly measure people' feelings and, thanks to the use of communication technologies, also to geographically locate these feelings. Unfortunately, the understanding is not as easy as one may think. Indeed, the large volume of data makes the manual approach impractical and the diversity of language combined with the brevity of the texts makes the automatic approach quite complicated. In this paper, we consider the gamification approach to sentimentally classify tweets and we propose TSentiment, a game with a purpose that uses human beings to classify the polarity of tweets (e.g., positive, negative, neutral) and their sentiment (e.g., joy, surprise, sadness, etc.). We created a dataset of more than 65,000 tweets, we developed a Web-based game and we asked students to play the game. Obtained results showed that the game approach was well accepted and thus it can be useful in scenarios where the identification of people' feelings may bring benefits to decision making processes

Semantic Partitioning and Machine Learning in Sentiment Analysis

“…Moreover, the memory caches will efficiently be used because the subset size is small enough to be stored in the cache, and then the partitioning can be achieved without accessing the slower main memory. Practically, it is well known that social media is a fertile environment, full of comments, emotions, thoughts, and opinions regarding common events and topics in society [67]. Integrating the SPAL model with social media will help organizations, companies, and governments in mining the hidden metadata, in order to improve the quality of their services to the end-users and to sustain human satisfaction [1].…”

National happiness index monitoring using Twitter for bilanguages

“…Kontopoulos et al (2013) proposed an ontology-based sentiment analysis for tweets. Furini and Montangero (2016) proposed and investigated a gamification approach for sentiment analysis from tweets. Bravo-Marquez et al (2016) investigated the transferring between words in sentiment lexicon and tweets' sentiments.…”

Sentiment Analysis in English Texts

“…The authors reported as well that after applying Semantic Analysis, the accuracy increased to reach 89.9. In [34], the authors analyzed sentiments by utilizing games. Authors introduced TSentiment, which is a web-based game.…”