Sentiment Analysis of Instagram

Sentiment analysis on social media is a Natural Language Processing practice used to extract subjective information and opinions from user-generated content on various social media platforms, such as Twitter, Facebook, and Instagram. The goal of the proposed work is to perform sentiment analysis on social media data related to a particular topic or brand, such as a product launch or a social issue. Social media data will be collected using relevant APIs or web scraping tools and pre-processed by cleaning and filtering out irrelevant or spam content This data is helpful for users as well as for the management to make informed decisions. Because micro-blog posts are usually very brief and informal, traditional opinion mining algorithms struggle to handle this type of content, making the subject challenging to tackle. Semantic and syntactic analysis in Sentiment Net was addressed in the previous lexicon-based sentiment analysis approach. The proposed suggestion is to produce an automation-based sentiment analysis method, which is less expensive in spite of this system's requirement is least. Consequently, in this work, introduces a novel system architecture that is capable of automatically analyzing the sentiments contained in these communications. We use this algorithm in conjunction with manually annotated social media data for sentiment analysis.