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Micro-blogs are a powerful tool to express an opinion. Twitter is one of the fastest growing micro-blogs and has more than 900 million users. Twitter is a rich source of opinion as users share their daily experience of life and respond to specific events using tweets on twitter. In this article, an automatic opinion classifier capable of automatically classifying tweets into different opinions expressed by them is developed. Also, a manually annotated corpus for opinion mining to be used by supervised learning algorithms is designed. An opinion classifier uses semantic, lexical, domain dependent, and context features for classification. Results obtained confirm competitive performance and the robustness of the system. Classifier accuracy is more than 75.05%, which is higher than the baseline accuracy.

Annotations are critical in various text mining tasks such as opinion mining, sentiment analysis, word sense disambiguation. Supervised learning algorithms start with the training of the classifier and require manually annotated datasets. However, manual annotations are often subjective, biased, onerous, and burdensome to develop; therefore, there is a need for automatic annotation. Automatic annotators automatically annotate the data for creating the training set for the supervised classifier, but lack subjectivity and ignore semantics of underlying textual structures. The objective of this research is to develop scalable and semantically rich automatic annotation system while incorporating domain dependent characteristics of the annotation process. The authors devised an enhanced bootstrapping algorithm for the automatic annotation of Tweets and employed distributional semantic models (LSA and Word2Vec) to augment the novel Bootstrapping algorithm and tested the proposed algorithm on the 12,000 crowd-sourced annotated Tweets and achieved a 68.56% accuracy which is higher than the baseline accuracy.

Micro-blogs are a powerful tool to express an opinion. Twitter is one of the fastest growing micro-blogs and has more than 900 million users. Twitter is a rich source of opinion as users share their daily experience of life and respond to specific events using tweets on twitter. In this article, an automatic opinion classifier capable of automatically classifying tweets into different opinions expressed by them is developed. Also, a manually annotated corpus for opinion mining to be used by supervised learning algorithms is designed. An opinion classifier uses semantic, lexical, domain dependent, and context features for classification. Results obtained confirm competitive performance and the robustness of the system. Classifier accuracy is more than 75.05%, which is higher than the baseline accuracy.