Handling Dimensionality of Ambiguity Using Ensemble Classification in Social Networks to Detect Multi-Label Sentiment Polarity - Sonawane, Sudarshan S.; Kolhe, Satish R. (Computer Assisted Methods in Engineering and Science, 2023)
With ever-increasing demand, social media platforms are rapidly developing to enable users to express and share their opinions on a variety of topics. Twitter is one such social media site. This platform enables a comprehensive view of the social media target setting, which may include products, social events, political scenarios, and administrative resolutions. The accessible tweets expressing the target audience’s perspective are frequently impacted by ambiguity caused by natural language processing (NLP) limitations. By classifying tweets according to their sentiment polarity, we can determine whether they express a good or negative point of view, a neutral opinion, or an input tweet that is irrelevant to the sentiment polarity context. Categorizing tweets according to their sentiment can assist future activities within the target domain in constructively evaluating the sentiment polarity and enabling improved decision-making based on the observed sentiment polarity. In this study, tweets that were previously categorized with one of the sentiment polarities were used to conduct predictive analytics of the new tweet to determine its sentiment polarity. The ambiguity of the tweets corpus utilized in the training phase is a critical limitation of the sentiment categorization procedure. While several recent models proposed sentiment classification algorithms, they confined themselves to two labels: positive and negative opinion, oblivious to the plague of ambiguity in the training corpus. In this regard, a novel multi-label classification of sentiment polarity called handling dimensionality of ambiguity using ensemble classification (HAD-EC) method, which diffuses ambiguity and thus minimizes false alerts, is proposed. The experimental assessment validates the HAD-EC approach by comparing the suggested model’s performance to other two existing models.