Sentiment detection in online content: A WordNet based approach

Abstract

Online Social Networks (OSN), such as Facebook, Twitter, Youtube and so on, are important sources of online content today. These platforms are used by millions of people world-wide, to share information and express their sentiment and opinion on various social issues. Sentiment analysis of online content – automatically inferring whether a particular textual content reflects a positive (e.g., happy) or negative (e.g., sad) sentiment of the person who posted the content – is an important research problem today, and has several potential applications such as analysing public opinion on various products or social issues. In this paper, we propose a simple but effective methodology of inferring the sentiment of textual content posted in online social media. Our approach is based on first identifying the positive/negative polarity of terms, i.e., whether a certain term (e.g., a word) is normally used in a positive or negative context, and then to infer the sentiment of a given text based on the polarity of the terms present in the text. A key challenge in this approach is that in online social media, different users use different words while expressing similar opinion. To address this, we use the well-known lexical database WordNet to identify groups of words which are synonymous to each other. We apply our proposed methodology on a large publicly available dataset containing content from six different online social media, which has been labeled as positive/negative by human annotators, and find that our methodology achieves better performance than several approaches developed earlier.