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Abstract
This paper covers all the essential details one must know about sentiment mining. It provides information on recent trends, applications of sentiment mining, different fields where it is used and also lot of useful information on the current research work being carried out in this area of data mining. Also, the basic workflow of the sentiment analysis process has been explained extraordinarily. Further, this paper also exemplifies the challenges and the future research being planned in the field of sentiment and opinion mining.
Keywords— Opinion Mining, Sentiment analysis, Aspect based Opinion Mining, Social Media Mining ,Mining News and Blog data
I. INTRODUCTION
Detection and extraction of opinions from online reviews is part of a new area of research developed in last decade. Opinion mining, also called in scientific literature as sentiment analysis, studies the determination and classification of opinions or feelings expressed in text, through the use of computing machines. The challenge of the research area is to extract knowledge from unstructured data. The reviews contain opinions expressed in natural language, common to people but uninterruptible by computers.
Social networking sites can easily provide one with all the information required to take a particular decision, for example, buying any item A from shopping sites. Social media is an excellent channel to put forward one’s opinion in front of the world. The data available in order to mine the opinion from it is magnificent. There have been many research projects based on the analysis of sentiments expressed on social media. Sentiment analysis poses newer and various challenges to gather information from the text in natural language. The region of Sentiment analysis aims to understand all the opinions expressed in natural language and categorize them.
Sentiment analysis is carried out on review sites and social medias like twitter where tweets gives us more accurate and varied opinions of the people from all over the world which can be about latest cellular phone like Iphone6. The reviews regarding a product would definitely affect the buyer’s decision.
1.TEXT MINING
Text Mining is the automated process of detecting and
revealing new, uncovered knowledge and inter-relationships
and patterns in unstructured textual data resources. Text mining targets un-discovered knowledge in huge amounts of
text. Whereas, search engines and Information Retrieval (IR)
systems have specific search target such as search query or
keywords and return related documents. This research
field utilizes data mining algorithms, such as classification,
clustering, association rules, and many more in exploring and
discovering new information and relationships in textual
sources.
2.SENTIMENT ANALYSIS
Sentiment analysis has been first introduced by Liu, It
is also known as opinion mining and subjectivity analysis is
the process to determine the attitude or polarity of opinions or
reviews written by humans to rate products or services.
Sentiment analysis can be applied on any textual form of
opinions such as blogs, reviews and Micro blogs. Micro blogs
are those small text messages such as tweets, a short message
that cannot exceed 149 characters. These micro blogs are
easier than other forms of opinions for sentiment analysis
[11]. Sentiment analysis can be done on a document level or a
sentence level. In the first case, the whole document is evaluated to determine the opinion polarity, where, the features describing the product/service should be extracted first. Where as, the second one, the document is divided into sentences each one is evaluated separately to determine the opinion polarity
3.APPLICATIONS
Sentiment mining covers a vast range of applications in several fields. These applications assist in making sense of hundreds of applications. Sentiment mining works diversely from the traditional survey methods and depends on listening in spite of asking which depicts more accurate reality. The main domain of applications implemented and managed by sentiment mining are as follows:
Applications to review related websites
It has the same capabilities as a review-related search engine and acts as an alternative to sites such as Opinions that collects information in the form of reviews and feedback. With such applications it becomes possible to summarize user reviews, fix blunders in user ratings and provide evidence that proves that user ratings was biased or those ratings need some correction.
.
Applications as sub-component technology
Sentiment analysis is having a potential role in supporting technologies used for other systems. Detecting ‘flames ‘in email or other means of communication, augmenting to recommendation systems which avoids recommending items getting lot of negative response, detecting web pages that includes sensitive information and averts displaying ads on those pages are some of the examples of application being executed in this field.
Applications in business and government intelligence
Sentiment mining is extremely important when business intelligence is the factor one is focusing on. In business and government intelligence, sentiment mining mainly handles reputation management, public relations and monitoring sources responsible for increment in negative or hostile communications. Extracting information helps organizations to develop better business strategies, find answers to their decline and failure, review their products based on people’s comments or tweets which would all help any organization walk on the path of success.
Using opinion Mining in Tourism
The domain of tourism extended activity online in the last decade. There are a lot of people that book accommodation online because is less time consuming, cheaper and they have the possibility to get detailed information about facilities and location of hotels. Concomitantly to development of online booking platforms, sites dedicated to presenting reviews in tourism also evolved. Booking sites also include sections with reviews about presented hotels
Concomitantly to development of online booking platforms, sites dedicated to presenting reviews in tourism also evolved. Booking sites also include sections with reviews about presented hotels. The advantage of having access to information and feedback, make users to prefer online booking. Studies about consumers online behavior revealed that the decision of acquiring a product is very much influenced by other buyers opinions (Bucur, 2014).
In the past one had trouble deciding to make a booking to a hotel not found in a guide or recommended by an agency, due to the lack of information. Now the problem is the excess of information. With so much sites providing rating and feedback, is impossible to read it all and, become extreme difficult to find the relevant information for one to get an overall image. Some sites only provide a rating system (by stars or numbers) or text reviews, others also provide a text review and a rating (Kasper & Vela, 2013).
A simple number on a rating system is not providing enough information, but neither a long review in which users express opinions about more than hotel features. There are a lot of reviews problems, which make them difficult to evaluate.
Some of them are:
Reviews are not concise
Scalar reviews make difficult to compare hotels with different services offered
Reviews refer to more than simple hotel accommodation
Totally different opinions from one user to another
Some aspects are more important so overall rating is not objective but more influenced on that aspects
Some reviews contains answers of hotel stuff to customers complains
4. WORK FLOW
4.1. 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
4.2. 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.
4.3. Analysis
Sentiment analysis is carried out on the data which is obtained after the pre-processing stage. From the sentiments contained in the data, the number of repetitions observed in the tweets and the location of the tweets is also analyzed. It is a vital stage in sentiment mining.
4.4. 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.
After all these stages are completed, the process of sentiment mining is successfully executed.
5. Components of Opinion Mining
There are mainly three components of Opinion Mining
• Opinion Holder : Opinion holder is the holder of a particular opinion; it may be a person or an organization that holds the opinion. In the case of blogs and reviews, opinion holders are those persons who write these reviews or blogs.
• Opinion Object : Opinion object is an object on which the
opinion holder is expressing the opinion.
• Opinion Orientation: Opinion orientation of an opinion on
an object determines whether the opinion of an opinion holder about an object is positive, negative or neutral
6. Tools available for Opinion mining
As we discussed in 4.1 there are various data sources are
available on web and mining those data is difficult task.
Main difficulty is extraction of emotions, structure of text,
form of data i.e. image or text, the language used on internet
for communication is vary from person to person or state
to state. So here are some ready to use tools for opinion
mining for various purposes like data preprocessing,
classification of text, clustering, opinion mining, sentiment
analysis etc.
The table no.1 shows the name of particular tool as well as
uses of these tools.