Abstract:
Now a day’s sentiment analysis is the most used research topic. The sentiment analysis result is
based on different investigation for example politics, terrorism, economy, international affairs,
movies, fashion, justice, humanity. Social media are the main resource for collecting people’s
opinion and their sentiment about a different trending topic. People use many abusing words in
social media to express their emotion. Using sentiment analysis, we will build a platform where
one can easily identify the opinions are either positive or negative or neutral. This research paper
will contain supervised learning which is under the machine learning approach. We run an
experiment on different queries from humanity to terrorism and find out an interesting result. First
of all, we have preprocessed the dataset to convert unstructured airline review into structured
review form. After that, we convert structured review into numerical value. We have to preprocess
the data before using it. Stop word removal, @ removal, Hashtag removal, POS tagging,
calculating sentiment score have done in preprocessing part. Then an algorithm has been applied
to classify the opinion as either it is positive or negative. In this research paper we will briefly
discuss supervised machine learning. Support vector machine as well as Naïve Bayes algorithm
and compares their overall accuracy, precession, recall value. The result shows that in case of
airline reviews Support vector machine gave way better result than Naïve Bayes algorithm.