What is Sentiment Analysis — Application, Benefits, and Limitations

Sentiment Analysis is a computational study or technique to distinguish positive and negative opinions from textual data programmatically. Many cutting-edge technologies like Natural Language Processing (NLP), Machine Learning (ML), Text Processing, and Deep Learning (DL) are being used nowadays to automate sentiment analysis. It allows to score within a quantified range — the positive, negative or neutral sentiment from a piece of text, with less human effort. Sentiment analysis may be applied in multiple areas such as customer feedback, movie or product reviews, and political comments. Large enterprises perform sentiment analysis to analyze public opinion, conduct market research, monitor brand, product reputation, and understand customer experiences. Various products often provide integration of sentiment analysis APIs/ plugins for customer experience management, social media monitoring, or workforce analysis, in order to deliver useful insights to their customers.

Just Sentiment Analysis is not Enough…

While sentiment analysis can help identify the sentiment behind an opinion or statement, there might be several aspects that have triggered the identified sentiment. And, that is a real challenge for the computer program. For instance, when analyzing reviews, it is easier to comprehend positive reviews than negative ones. Also, it requires determining the intended ‘aspect’ of the review that has generated a negative opinion. It is possible that the customer in a restaurant/ hotel has given a completely negative review just because of the rude behaviour of one of the staff members but liked the food quality. In such a case, the negative feedback for the staff of the hotel outweighs positive feedback related to food. This is where Aspect Based Sentiment Analysis saves the day.

Decoding Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis (ABSA) is a technique that takes into consideration the terms related to the aspects and identifies the sentiment associated with each aspect. ABSA model requires aspect categories and its corresponding aspect terms to extract sentiment for each aspect from the text corpus. One can create a domain-specific model for a specific implementation; however, general language models can also be used. Typical ABSA requires labeled data containing aspect terms and aspect categories for each statement along with its sentiment score. However, it can be solved using the unsupervised approach without having labeled data and a list of aspect terms. For example, what was the overall experience of customers with the hotel staff, food variety, price, taste, and location? A business needs to identify the aspects of the product/service that attract more customers and/or keep away people to use/ buy the product/ service. ABSA identifies sentiment for each aspect category i.e., hotel staff, food variety, price, taste, and location. It helps business to track how end-users sentiment changes toward specific features and attributes of a service or product