International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
SENTIMENT ANALYSIS OF TWITTER DATA V.Lakshmi, K.Harika , H.Bavishya, Ch.Sri Harsha Under the guidance of M.Ramesh, Asst prof. Department of Information Technology VR Siddhartha engineering college, Andhra Pradesh, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract – With the advancement of web technology and its
services in such a way that it can be offered as per the users requirements.
growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly structured, heterogeneous and are either positive or negative, or neutral in some cases.
1.1 Twitter’s simplicity Twitter data is interesting because tweets happen at the “speed of thought” and are available for consumption in real time, and you can obtain data from anywhere in the world. We chose because Twitter is predominantly suited for data mining because of the three key features.
Key Words: Twitter, Sentiment analysis(SA), Opinion mining, Machine learning, Naïve Bayes(NB).
Twitter’s API is well desighned and easy to access.
Twitter data in a convenient format for analysis.
Twitter’s terms of use for the data are relatively liberal as compared to other API’s.
1.2 Twitter’s API 1.INTRODUCTION
An Application Programming Interface(API) is a set of programming instructions and standards for accessing a web-based software application. Twitter bases its API of the Reprsentational State Transfer(REST) Architecture. REST architecture refers to a collection of network design principles that define resources and ways to address and access data.
Now-a-days, the age of internet has changed the way people express their views, opinions. It is now mainly done through blog posts, online forums, product review websites, social media etc. Nowadays, millions of people are using social network sites like Facebook, Twitter, Google plus, etc to express their emotions, opinion and share views about their lives.
2. LITERATURE SURVEY
Through the online communities, we get an interactive media where consumers inform and influence others through forums. Social media is generating a large volume of sentiment rich data in the form of tweets, status updates, blog posts, comments, reviews, etc. Moreover, social media provides an opportunity for business by giving a platform to connect with their customers for advertising. People mostly depend upon user generated content over online to a great extent for decision making. For e.g. if someone wants to buy a product or wants to use any service, then they firstly look up its reviews online, discuss about it on social network but the data generated by users is too vast for a normal user to analyse. So there is a need to automate this, various sentiment analysis techniques are widely used. Sentiment analysis (SA) tells user whether the information about the product is satisfactory or not before they buy it. Marketers and firms use this analysis data to understand products or
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In recent years a lot of work has been done in the field of “Sentiment Analysis on Twitter” by number of researchers. In its early stage it was intended for binary classification which assigns opinions or reviews to bipolar classes such as positive or negative only. Pak and Paroubek(2010) proposed a model to classify the tweets as objective, positive and negative. They created a twitter corpus by collecting tweets using Twitter API and automatically annotating those tweets using emoticons. Using that corpus, they developed a sentiment classifier based on the multinomial Naïve Bayes method that uses features like Ngram and POS-tags. The training set they used was less efficient since it contains only tweets having emoticons.
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