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Saturday, May 29, 2021

Comparision of Sentiment Analyses Methods

An approach we coded long before neural methods.    Here an excellent look at current methods.  Pointers to code.

Sentiment Analysis — Comparing 3 Common Approaches: Naive Bayes, LSTM, and VADER

A Study on Strengths and Drawbacks for the Different Approaches (With Sample Code)

By Kevin C Lee

 Sentiment Analysis, or Opinion Mining, is a subfield of NLP (Natural Language Processing) that aims to extract attitudes, appraisals, opinions, and emotions from text. Inspired by the rapid migration of customer interactions to digital formats e.g. emails, chat rooms, social media posts, comments, reviews, and surveys, Sentiment Analysis has become an integral part of analytics organizations must perform to understand how they are positioned in the market. To be clear, Sentiment Analysis isn’t a novel concept. In fact, it has always been an important part of CRM (Customer Relationship Management) and Market Research — companies rely on knowing their customers better to evolve and innovate. The more recent rise is driven largely by the availability/accessibility of customer interaction records and well as improved computing capabilities to process these data. This advancement has really benefited consumers in meaningful ways. More than ever, organizations are listening to their constituents to improve. There are numerous approaches for Sentiment Analysis. In this article, we’ll explore three such approaches: 1) Naive Bayes, 2) Deep Learning LSTM, and 3) Pre-Trained Rule-Based VADER Models. We will focus on comparing simple out-of-the-box version of the models with the recognition that each approach can be tuned to improve performance. The intention is not to go into great details about how each methodology works but rather a conceptual study on how they compare to help determine when one should be preferred over another. .. "

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