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Opinion Mining and Sentiment Analysis

Introduction

There’s a lot of buzzword around the term “Sentiment Analysis” and the various ways of doing it. Great! So you report with reasonable accuracy what the sentiment about a particular brand or product is.
Opinion Mining is about having a deeper understanding of the review that was written. Typically, a detailed review will not just have a sentiment attached to it. It will have information and valuable feedback that can literally help to build the next strategy. Over time, some powerful methods have been developed using Natural Language Processing and computational linguistics to extract these subjective opinions.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. Opinion Mining and Sentiment Analysis covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. The focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. The survey includes an enumeration of the various applications, a look at general challenges and discusses categorization, extraction and summarization. Finally, it moves beyond just the technical issues, devoting significant attention to the broader implications that the development of opinion-oriented information-access services have: questions of privacy, vulnerability to manipulation, and whether or not reviews can have measurable economic impact. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided. Opinion Mining and Sentiment Analysis is the first such comprehensive survey of this vibrant and important research area and will be of interest to anyone with an interest in opinion-oriented information-seeking systems.

Procedure

Extraction

In the extraction phase of Sentiment mining, social media acts as a source of data. In order to explain this process easily further details are in resemblance with twitter. In twitter, number of users gives their reviews by posting messages which are called as tweets. These tweets depict the sentiments of the users. The process of sentiment mining is basically analyzing this data and converting it into knowledge. Following are few fundamental characteristics observed in the data while performing extraction:
  •  The length of the twitter message is limited to 140 characters.
  •  Moreover, we observe the presence of spelling errors and informal or cyber slang in these messages.
  • The amount of data available is copious and as most of the twitter messages are available in public domain it can be used for the purpose of sentiment mining.

Data extracted from the social media like twitter is updated very frequently. Therefore, it helps to give the feeling of real time representation of the sentiments. In order to obtain the data on run-time an internet bot can be used known as web crawler. A web crawler browses through the World Wide Web in organized manner to index the web pages. It is one of the many fundamental components which constitute web search engines. The indexing of the web pages grants the user to issue queries and get the required pages as per the query issued.

Pre-Processing

As the name suggests, in this phase of sentiment analysis processing of the data is carried out. In the pre-processing stage, the extracted data is cleaned as it contains large amount of noise before sending the text for analyzing. The extracted text contains lot of grammatical errors as the text is of limited length. Pre-processing of the data is necessary and it is a crucial part as one needs to make sure that the unnecessary part of the text is removed and the relevant part of the text which stores the sentiment of the user is not removed. Following are few techniques explained in brief, keeping in mind twitter as the social media used, which are generally used in order to draw information for sentiment mining.

Knowledge Discovery

To find the opinion of the people with respect to any particular occurrence, it is essential to store the data which is related to the event. Once the polarity of the sentiments is known it can be used to generate statistical graphs and charts. The knowledge gathered from these electronic texts from the web when shown in graphs would aid the individuals in making decision as it would show the polarity of the sentiments of the individuals and to what extent it can be followed by referring to the graphs.

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