My Project tittled 'Twitter Sentimental Analysis' is a web based project which uses the machine learning onecepts & algorithms to predict the sentiments of the tweets on the basis the data given from the dataset is in the form of comma separated values(csv) files with “tweets” and their corresponding sentiments.
In my project user enters the keyword he/she like to analysis,machine predicts the around 100 tweets related to the keyword and map it with the given data sets.On the basis of Polarity is a float value within the range [-1.0 to 1.0] where 0 indicates neutral, +1 indicates a very positive sentiment and -1 represents a very negative sentiment.I have used following categcories to classify the sentiment of tweets very exclusive :
positive = 0
wpositive = 0
spositive = 0
negative = 0
wnegative = 0
snegative = 0
neutral = 0
Now, i would like to give a breif about the libraries that i used in my project:
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TEXTBLOB, TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.
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Tweepy is open-sourced libarary created by active developer community, hosted on GitHub and enables Python to communicate with Twitter platform and use its API.
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Matplotlib, it's a multi platform data visualization library which gives benefits of visual access to huge amounts of data in very digestable visuals.We can generate plots,histograms, power spectra, bar charts,error charts,scatterplots etc.
Though it all sounds very technical. However my dear friends this has an amazing future scope.Present Sentiments hold a key to the future events. To make it sound a bit technical, you can say that the sentiments represent the "present value of future events". Now this value can have deep social, political and monetary significance. It can be "Expression of opinion about a public figure", "opinions expressed through tweets before elections",or "the buzz before a movie release", all these can be great cues for things to come.
Therefore when people comment about present news stories, the sentiment analysis can actually offer a key to predict the future outcomes or atleast anticipate them better!