International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 04 | Apr -2017
p-ISSN: 2395-0072
www.irjet.net
Classification of Epistles using Distant Supervision Sailaja.Gullipalli, Sravani.Grandhi, Santosh.Nimmaka, Asst.Proffessor, Dept of CSE, Thandra Paparaya Institute & Technology, JNTU, Kakinada, A.P, India. B.Tech Student, Dept of CSE, Thandra Paparaya Institute & Technology, JNTU, Kakinada, A.P, India. B.Tech Student, Dept of CSE, Thandra Paparaya Institute & Technology, JNTU, Kakinada, A.P, India.
ABSTRACT: We introduce a novel approach for automatically classifying the sentiment of Twitter messages. These messages are classified as either positive or negative with respect to a query term. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. There is no previous research on classifying sentiment of messages on microblogging services like Twitter. We present the results of machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision. Our training data consists of Twitter messages with emoticons, which are used as noisy labels. This type of training data is abundantly available and can be obtained through automated means. We show that machine learning algorithms (Naive Bayes, Maximum Entropy, and SVM) have accuracy above 80% when trained with emoticon data. This paper also describes the preprocessing steps needed in order to achieve high accuracy. The main contribution of this paper is the idea of using tweets with emoticons for distant supervised learning.
Categories and Subject Descriptors [Artificial Intelligence]: Natural Language Processing
General Terms Algorithms
Keywords Twitter, sentiment analysis, sentiment classification
1. INTRODUCTION Twitter is a popular microblogging service where users create status messages (called “tweets�). These tweets some- times express opinions about different topics. We propose a method to automatically extract sentiment (positive or negative) from a tweet. This is very useful because it al- lows feedback to be aggregated without manual intervention.
Consumers can use sentiment analysis to research products or services before making a purchase. Marketers can use this to research public opinion of their company and products, or to analyze customer satisfaction. Organizations can also use this to gather critical feedback about problems in newly released products. There has been a large amount of research in the area of sentiment classification. Traditionally most of it has focused on classifying larger pieces of text, like reviews. Tweets (and microblogs in general) are different from reviews primarily because of their purpose: while reviews represent summarized thoughts of authors, tweets are more casual and limited to 140 characters of text. Generally, tweets are not as thoughtfully composed as reviews. Yet, they still offer companies an additional avenue to gather feedback. There has been some work by researchers in the area of phrase level and sentence level sentiment classification recently. Previous research on analyzing blog posts includes Previous research in sentiment analysis like Pang et al. have analyzed the performance of different classifiers on movie reviews. The work of Pang et al. has served as a baseline and many authors have used the techniques provided in their paper across different domains. Pang et al. also make use of a similar idea as ours, using star ratings as polarity signals in their training data. We show that we can produce comparable results on tweets with distant supervision. In order to train a classifier, supervised learning usually requires hand-labeled training data. With the large range of topics discussed on Twitter, it would be very difficult to manually collect enough data to train a sentiment classifier for tweets. Our solution is to use distant supervision, in which our training data consists of tweets with emoticons. This approach was introduced by Read. The emoticons serve as noisy labels. For example, :) in a tweet indicates that the tweet contains positive sentiment and :( indicates that the tweet contains negative sentiment. With the help of the Twitter API, it is easy to extract large amounts of tweets with emoticons in them. This is a significant improvement over the many hours it may otherwise take to hand-label training data. We run classifiers trained on emoticon data against a test set of tweets (which may or may not have emoticons in them). We present the results of our experiments and our thoughts on how to further improve results. To help visualize the util-
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