Abstract
Pervasive sensing of people's opinions is becoming critical in strategic decision processes, as it may be helpful in identifying problems and strengthening strategies. A recent research trend is to understand users' opinions through a sentiment analysis of contents published in the Twitter platform. This approach involves two challenges: the large volume of available data and the large variety of used languages combined with the brevity of texts. The former makes manual analysis unreasonable, whereas the latter complicates any type of automatic analysis. Since sentiment analysis is a difficult process for computers, but it is quite simple for humans, in this article, we transform the sentiment analysis process into a game. Indeed, we consider the game with a purpose approach and we propose a game that involves users in classifying the polarity (e.g., positive, negative, neutral) and the sentiment (e.g., joy, surprise, sadness) of tweets. To evaluate the proposal, we used a dataset of 52,877 tweets, we developed a Web-based game, we invited people to play the game, and we validated the results through two different methods: ground-truth and manual assessment. The obtained results showed that the game approach is effective in measuring people' sentiments and also highlighted that participants liked to play the game.
Highlights
The understanding of people’ sentiments is a critical factor in strategic decision processes, as it may be helpful in identifying problems and strengthening strategies
We first present studies that give classifications of the possible sentiments people may feel, we focus on the recent approaches that aim to transform users’ textual contents into users’ sentiments and, we overview some Game With A Purpose studies in which a gamification approach is used to solve problems that are easy for humans, but difficult for computers
We perform an evaluation similar to the one done in the ESP Games With A Purpose (GWAP) game [38] and we consider two different distinct evaluations: groundtruth and manual assessment
Summary
The understanding of people’ sentiments is a critical factor in strategic decision processes, as it may be helpful in identifying problems and strengthening strategies. Effective in most cases, this methodology has some limits: it is expensive and time consuming To overcome these limitations, nowadays researchers are proposing methods that focus on contents posted on social media platforms like Twitter [6, 7]. In social media platforms, users’ generated contents are associated with metadata like OS language, device type, capture time and geographical location [8, 9]. This means that Twitter contents provide a wealth of opportunities for understanding people’s opinions and feelings about services, brands, events, etc. To understand the sentiments of people it is necessary to define which possible emotions a human being may feel.
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