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Research proposal on Emotion Classification using Deep Learning Models

Research proposal on Emotion Classification using Deep Learning Models

 The advent of Information Communication Technology (ICT) especially, social networking sites, enables users to upload and share their opinions over the Internet. In consequence, the massive influx of natural language text in terms of reviews, forum discussions, microblogs, and blogs that have become available online. Several organizations and individuals seek to utilize this natural language information for better decision-making due to the fact of human behaviors that heavily reflects their opinion. Therefore, emotion classification or opinion mining is necessary to recognize the emotion of the people from their unstructured natural language reviews. The opinion mining also referred to sentiment analysis attempts to filer the haphazardly scattered emotion from the heterogeneous reviews.
 In the context of opinion mining for an organization, there is no need to conduct opinion polls, surveys, and focus groups for capturing public opinions since there is plenty of such data is publicly available. Opinion mining is an automated data mining process geared towards revealing and making sense of the emotions and moods of people. The emotion classification imparts beneficial results in commercial applications like product feedback, product reviews, marketing analysis, news articles or political debates, and customer service.
 Opinion mining continues to evolve and continues to improve across time. Despite it still leaves some challenges in recognizing peoples’ emotions or sentiment analysis. The primary challenge is sarcasm detection in the data that reveals the negative emotion using only the positive text, which induces the complexity in gauging the proper sentiment. Another critical bottleneck in the emotion classification is the domain dependency of emotion text, whereas the one feature set yields best results in one domain; on the other hand, it lacks to perform well in some other domain. Moreover, the inadequacy of labeled data poses an obstacle to the advancement of the field.
 The diverse application gets to benefit from the emotional classification. However, it does not perform accurately while detecting the sentiments from the free-form text of humans. The emotion classification system confronts the complexity while dealing with scarce or unclear datasets. Furthermore, multi-lingual sentiment analysis, data sparsity, subject detection still leave the challenges for recognizing the emotions that hinder the opinion mining task. Some of the research works lack dictionaries, lexicon, and corpora. Also, the critical problem in emotion classification is that they lack identifying the spam opinion from the reviews. This fake opinion of the reviews, along with the diverse writing style, misleads the automated emotion classification system by providing untruthful positive or negative opinions.