Abstract
In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.
Highlights
Nowadays, people are very expressive on the web
That is why aspect based sentiment analysis has gained popularity, and a lot of work has been done in this area in the last decade
Latent Dirichlet Allocation (LDA) assumes that every document is a mixture of topics, and every word has a certain probability of falling into a particular topic
Summary
Due to the exponential growth in user feedback data, it becomes necessary for every product and service provider to perform the mining of these feedbacks People regularly share their views on current activities on Twitter or similar platforms. That is why aspect based sentiment analysis has gained popularity, and a lot of work has been done in this area in the last decade Still, it is an active research area, especially unsupervised approaches that require improvements (Yue et al ,2019, Do et al , 2019). LDA assumes that every document is a mixture of topics, and every word has a certain probability of falling into a particular topic. In LDA, each word in each document comes from a topic.
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