Abstract
Today Micro-blogging has become a popular Internet-user communication tool. Millions of users exchange views on different aspects of their lives. Thus micro blogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Due to the recent emergence of micro blogging, there are a few research works devoted to this subject. We concentrate in our paper on Twitter, one of the prominent micro blogging sites to analyze sentiment of the public. We'll demonstrate, how to gather real-time twitter data for sentiment analysis or opinion mining purposes, and employed algorithms like Term Frequency - Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Multinomial Naive Bayes ( MNB). We are able to determine positive and negative sentiments for the real-time twitter data using the above chosen algorithms. Experimental evaluations below shows that the algorithms used are efficient and it can be used as a application in detection of the depression of the people. We worked with English in this article, but for any other language it can be used.
Highlights
Micro blogging[1] has become one of the most commonly recognized channels of communication used by people across the globe
Text messages appear daily on popular websites offering micro blogging services such as Reddit, Pinterest, Facebook, Twitter and so on. Such text messages are released on social networks in order to share views on different issues and address the latest problems Due to the unlimited formation of messages and the simple accessibility of microblogging sites, Internet users have been inclined from conventional communication to microbloggingThe rapid increase in the number of user posts about consumer goods and services or views on political and religious issues is making microblogging sites such as Twitter more common for analyzing sentiments
This paper presented the results of the Machine learning(ML) algorithms used by distant supervision to characterize the Twitter messages sentiments
Summary
Micro blogging[1] has become one of the most commonly recognized channels of communication used by people across the globe. Text messages appear daily on popular websites offering micro blogging services such as Reddit, Pinterest, Facebook, Twitter and so on. Such text messages are released on social networks in order to share views on different issues and address the latest problems Due to the unlimited formation of messages and the simple accessibility of microblogging sites, Internet users have been inclined from conventional communication to microbloggingThe rapid increase in the number of user posts about consumer goods and services or views on political and religious issues is making microblogging sites such as Twitter more common for analyzing sentiments. To forecast stock market movements, we use twitter data to predict people's mood and use expected sentiment and the DJIA values of previous days. [3] Sentiment analysis of movie reviews in Twitter using machine learning techniques: In this nominal we examined Movie reviews using different Machine Learning (ML) techniques such as Naive Bayes, K-Nearest Neighbor and Random Forest. The Naïve Bayes classifier (NBC) achieved accuracy of 81.45%, the Random Forest classifier achieved accuracy of 78.65%, the K-Nearest Neighbor classifier achieved accuracy of 55.30%[7]
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