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.
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Notes
- 1.
Messages having equal number of positive and negative emoticons are ignored.
- 2.
Note that the study [9] studied one more approach (apart from the ones shown in Table 6), where the polarity of a text is directly given based on the emoticons contained in the text. Since less than 10 % of the text messages in this dataset (as well as in online social media in general) contain emoticons [9, 13], this approach can be used for only 10 % of the messages (as also observed in [9]). Hence, we do not consider this approach for comparison.
- 3.
Error analysis on the other datasets also yielded similar observations (omitted for brevity).
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Acknowledgement
The authors thank the anonymous reviewers for their constructive suggestions, and the authors of [9] for sharing the annotated datasets.
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Dutta, S., Roy, M., Das, A.K., Ghosh, S. (2015). Sentiment Detection in Online Content: A WordNet Based Approach. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_36
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