Analysis of Market Trends with Sentimental Analysis using Word2Vec and GloVe

This research study implements an aspect-based sentimental analysis of online comments and reviews. It considers customers' opinions based on various aspects of a product such as quality, price, and customer service. Extricating the sentiments related to the product's features gives a nuanced perception of the feedback provided by the customer. This enables the company to filter comments based on the most appropriate details, making it simple to access the areas needing enhancement. The proposed solution uses machine learning techniques like Word2Vec, GloVe etc to inquire into text identifying the important aspects in reviews by categorizing them as positive, negative, or neutral. The system then combines the data to provide deep comprehension enabling the business to acknowledge not just the sentiment but also the sentiment related to each feature. The machine learning model SVM is used to analysis the sentiment of the user comment. The feedback linked with the sentiments, it helps the stakeholders to point out trends and the areas to develop. This allows the companies to make datadriven conclusions swiftly and efficiently. This allows the companies to make data-driven conclusions swiftly and efficiently. This aspect-specific feedback enables stakeholders to identify areas that require improvement to adapt to the trends. This model allows room for enterprises to make room to make informed and data-driven decisions swiftly and effectively which helps their ability to respond to the needs of customers and products.