Sentiment Analyzer 

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Data Science Project.

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Built a sentiment analyzer using the Natural Language Toolkit (NLTK) package, a commonly used NLP(Natural Language Processing) library in Python, to analyze textual data. Prepared a dataset of sample tweets from the NLTK package for NLP. Tested and trained the machine learning model on pre-classified tweets and used the model to classify the sample reviews into negative and positives sentiments.

 

Technology used: Python 3 (version 3.6.5)

 

Project Duration: 5 months

 

Problem Statement: In sentiment analysis we classify the polarity of a given text at the document, sentence, or feature /aspect level; whether the expressed opinion in a document, sentence or an entity feature/aspect is positive or negative.

 

Solution Approach: Sentiment analysis is the automated process of understanding the sentiment or opinion of a given text. This machine learning tool can provide insights by automatically analyzing product reviews and separating them into tags: Positive, Neutral, Negative

 

Key Benefits:

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• Sentiment analysis of product reviews helps improve customer service by tracking the key messages from customers' opinions and thoughts about a brand.

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• Developing Quality Products by analyzing customer opinions about a brand simplifies the process of maintaining customer loyalty and provides opportunities for improvement.

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• With more data and information gathered through sentiment analysis, the organizations could develop an effective marketing strategy

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• Improve Media Perceptions by tracking the understanding of the journalists, writers, columnists, market analysts, media researchers or independent contributors towards the company, be it the product, service, company values, human resources etc.

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• Increasing Sales Revenue

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• Frequent monitoring of the customers’ responses or opinions towards a brand would help to identify any issues quickly and improve crises management.​

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