OPINION MINING, What are you thinking?

It has always been important to any business or individual about the thoughts on various topics , whether it is an item , person etc . each persons though has always been an important piece of information
For example : Which college to study?
Which Phone to buy?
and so on .We usually we rely on social media ( blogs , Facebook posts , reviews etc) to make our choices or from a person who we rely on .
but is every information we read true and reliable?
this leads us to an important concept called sentimental analysis or opinion mining


 Sentiment analysis (also known as opinion mining refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials.Sentiment analysis is widely applied to reviews and
social media for a variety of applications, ranging from marketing to customer service.



Naive Bayes

In machine learning, naive Bayes classifiers are a family of
simple probabilistic classifiers based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. It is one of the most basic text classification techniques with various applications in email spam detection, personal email sorting, document categorization, sexually explicit content detection, language detection and sentiment detection. Despite the naive design and oversimplified assumptions that this technique uses, Naive Bayes performs well in many complex
real-world problems.

An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters necessary for classification.

Abstractly, naive Bayes is a conditional probability model: given a problem instance to be classified, represented by a vector x = ( x 1 , … , x n ) representing some n features independent variables), it assigns to this instance probabilities:


for each of K possible outcomes or classes Ck.

The problem with the above formulation is that if the number of features n is large or if a feature can take on a large number of values, then basing such a model on probability tables is infeasible. We therefore reformulate the model to make it more tractable.We therefore reformulate the model to make it more tractable. Using Bayes’ theorem, the conditional probability can be decomposed as

P(ck|x)=P(ck).P(x|ck) / P(x)



Sentiment analysis is in demand because of its efficiency. Thousands of text documents can be processed for sentiment (and other features including named entities, topics, themes, etc.) in seconds, compared to the hours it would take a team of people to manually complete. Because it is so efficient (and accurate – Semantria  has 80% accuracy for English content) many businesses are adopting text and sentiment analysis and incorporating it into their processes.

By Divya J. and Shruthi B. S.


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