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This project forked from ikigai-aa/amazon-products-sentiment-analysis

Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback. Sentiment analysis is a text analysis method that detects polarity (e.g. a positive or negative opinion) within text, whether a whole document, paragraph, sentence, or clause. Why Perform Sentiment Analysis? It’s estimated that 80% of the world’s data is unstructured, in other words it’s unorganized. Huge volumes of text data (emails, support tickets, chats, social media conversations, surveys, articles, documents, etc), is created every day but it’s hard to analyze, understand, and sort through, not to mention time-consuming and expensive. Sentiment analysis, however, helps businesses make sense of all this unstructured text by automatically tagging it. Benefits of sentiment analysis include: Sorting Data at Scale Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys? There’s just too much data to process manually. Sentiment analysis helps businesses process huge amounts of data in an efficient and cost-effective way. Real-Time Analysis Sentiment analysis can identify critical issues in real-time, for example is a PR crisis on social media escalating? Is an angry customer about to churn? Sentiment analysis models can help you immediately identify these kinds of situations and gauge brand sentiment, so you can take action right away. Consistent criteria It’s estimated that people only agree around 60-65% of the time when determining the sentiment of a particular text. Tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs. By using a centralized sentiment analysis system, companies can apply the same criteria to all of their data, helping them improve accuracy and gain better insights.

License: Apache License 2.0