Success of any company or product depends on customer’s satisfaction. If customers do not satisfied with the services or product provided by company, then certainly company needs to improve it. Opinion mining (OM) can help in doing this. OM is the process of computationally identifying and categorizing opinions from piece of text and determines whether the writer’s attitude towards a particular topic or the product is positive, negative or neutral. This paper proposed a training model using sentdex data set to train the OM algorithm. This algorithm is based on supervised machine learning model to calculate OM of given text. Entire system is developed to calculate opinion from tweeters feeds. This system is working on real time data. Proposed system is designed for open field. One can take opinion of many field like political issue, product, company, person etc. this paper also presented the comparison of proposed results with well known python textblob API. textblob is used to perform many texts based operations. Sentiment analysis (OM) is one of them. In many OM systems this API is used.

Opinion Mining, Machine Learning, NLP, textblob, sentdex, NLTK

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