A Study On Sentiment Analysis Methods And Tools

International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2014): 5.611
Volume 4 Issue 12, December 2015
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Licensed Under Creative Commons Attribution CC BY
A Study on Sentiment Analysis: Methods and Tools
Abhishek Kaushik1
, Anchal Kaushik2
, Sudhanshu Naithani3
1
Kiel University of Applied Sciences, Computer and Electrical Department, Sokratesplatz 1, 24149 Kiel, Germany
2
Amity University, Department of Management Studies, Sector 125, Noida, India
3
Kurukshetra University, Department of Computer Science, Thanesar Taluk, Kurukshetra, India
Abstract: The purpose of social media has created many chances for people to publicly voice their beliefs, simply when they are
employed to deliver an opinion hit a vital problem. Sentiment Analysis is a case of natural language processing which could mark the
mood of the people about any specific product by analysis. Sentiment Analysis is a process of automatic extraction of features by mode
of notions of others about specific product, services or experience. The Sentiment Analysis tool is to function on a series of expressions
for a given item based on the quality and features. Sentiment analysis is also called Opinion mining due to the significant volume of
opinion. Analyzing customer opinion is very important to rate the product. To automate rate the opinions in the form of unstructured
data is been a challenging problem today. Thus, this paper discusses about Sentiment analysis methods and tools used.
Keywords: Data Mining, Opinion Mining, Opinion Summarization, Sentiment Analysis, Text Mining, Web Mining.
Figure 1: Hierarchy of Data Mining
1. Introduction
The era of electronic information in every phase of life is
evolving rapidly, which tends to produce a large number of
data. As an outcome huge volumes of data are generated in
field of technology, business, healthcare, tourism, e-
marketing, etc. Automated analysis systems are meant for
analysis, summarization and classification of data and
number of efficient methods to store huge amount of data.
Text mining is an approach used different fields like machine
learning, information retrieval, statistics, and computational
linguistics for opinion mining. Web mining is a subset of text
mining used to mine the unstructured web data in the form
Content mining, Web Structure mining and Web Usage
mining. The aim of sentiment analysis is to make an
automated machine able to recognize and categorize
emotions [2]. A thought, view, or attitude based on emotion
instead of reason is called sentiment. Figure 1 shows the
different sub level of Data Mining and the branches of
sentiment analysis.
2. Literature Overview
Bakhtawar Seerat et al [15] proposed the method of opinions
extraction from an online web page and the limitation of
Sentiment analysis. Meena Rambocas [20] concluded all the
challenges marketers can face when using sentiment analysis
as an alternative technique capable of triangulating
qualitative and quantitative methods through innovative real
time data collection and analysis. G.Vinodhini et al [10]
proposed an Overview of different opinion mining
techniques. Blessy Selvam et al [3] proposed different
approaches of sentiment classification and the existing
methods with the framework. Rudy Prabowo [16] formed a
new approach by combining rule-based classification,
supervised learning and machine learning and tested it on
movie reviews, product reviews and MySpace comments.
And also proposed a semi automatic approach to get better
effectiveness. Archana Shukla [19] introduced a tool to tell
the quality of the document or its usefulness based on the
annotations. Ayesha Rashid et al [1] presented the limitations
on different sentiment level and the methods used in
sentiment analysis. Dongjoo Lee et al [4] proposed to use the
PMI method to use for large corpus to achieve higher
accuracy. Dr.Ritu Sindhu et al [14] presented different levels
of analysis and issues in sentiment analysis. S.Chandrakala et
al [5] proposed a work on recent papers on sentiment
analysis and its related tasks with future challenges. Bo Pang
[17] gave a new machine learning method that determines
sentiment polarity. Arti Buche et al [11] proposed the Naive
Bayes algorithm and also Hidden Markov Model to calculate
the Entropy and Purity measure in string mining. S.Padmaja
et al [6] proposed a work on Machine Learning Models for
text classification. . Nile M. Shrike et al [13] compared the
Paper ID: NOV151832 287

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accuracy using Bayes, Maximum Entropy and Support
Vector Machine. Raisa Varghese et al [7] proposed the
structure of sentiment analysis. Vijay B . Roth et al [9] have
compared the synopsis of different approaches used for
sentiment analysis. Nidhi Mishra et al [12] proposed the
inner view of sentiment analysis at different levels David
Osimo et al [2] proposed an outline for a new Research
Challenge on Sentiment Analysis. Sindhu, Chandrakala et al
[8] proposed a systematic flow and Machine learning
approaches to optimize the performance. Alec Go [18]
proposed a novel approach to classify sentiment of the twitter
message automatically and showed that machine learning
algorithms (Naive Bayes, Maximum Entropy, and SVM)
have accuracies above 80% when trained with emoticon data.
3. Information Source
User view is an important factor for the improvement of the
quality of services. Blogs, review sites, data and micro blogs
provide a good information of the products and services
provided to clients.
Blogs: The name relates all the blog sites is called
blogosphere [1]. People express about their thoughts they
want to share with others on a blog. Blog pages [1] have
become the popular platform to share ones personal views
about specific products .
Review sites: The opinions of others is being an important
factor while purchasing anything. A large number of users
express their views on a particular product. These reviews
are easily available on the Internet. The re-viewer’s data used
in most of the opinion classification gather from the e-
commerce websites [10] like www.flipkart.com .
Data Set: The dataset contains different types of product
reviews (including Books, DVDs, Electronics and Kitchen
appliances) and movie reviews extracted from Flip-kart and
IMDB webpage.
4. Sentiment Analysis
Sentiment analysis is a technique which is used to extract the
meaningful information in the documents [6]. In general,
opinion mining tries to figure out the sentiment of a writer
about some specific aspect and also the overall contextual
polarity of a document. The sentiment may be a judgment,
mood or evaluation of the writer [2]. A core issue in this field
is an opinion classification, where a review is classified as a
positive or negative evaluation of a subjected object (film,
book, etc.). The assessment of sentiment can be done in two
ways:
4.1 Direct opinions: It gives positive or negative sentiment
about the product directly [12]. For example, “The food
quality of this hotel is poor” expresses a direct opinion.
4.2 Comparison: It means to compare the subject with any
other similar objects [12]. For example, “The food quality of
the hotel-a is better than that of hotel-b.” expresses a
comparison. Figure 2 had a workflow of Opinion Mining.
The views are being extracted from writers review over their
comment. Opinion feature extraction is a sub-process of
opinion mining [15]. Pre-processing In this process, raw data
taken and is pre-processed for feature extraction.
Figure 2: Work flow of Opinion Mining
The preprocessing phase [1] has been further divided into a
number of sub phases as follows:
Tokenization is the process to split up into tokens by
removing white spaces, commas and other symbols, etc. Stop
word Removal removes words (like „a, an, the, of, for,).
Stemming reduce the relevant tokens into a single type.
Normalization is a process that has English texts to be
published in both higher and lower case characters and turns
the entire document or sentences into lowercase/uppercase.
Feature extraction phase deals with feature types [3] (which
identifies the type of features used for opinion mining),
feature selection (used to select good features for opinion
classification), feature weighting mechanism (weights each
feature for good recommendation) reduction mechanisms
(features for optimizing the classification process).
Types of features used for opinion mining could be:
1)Term frequency (The presence of the term in a document
carries a weight age).
2)Term co-occurrence (features which occurs together like
uni-gram, bi-gram or n-gram),
3)Part of speech information (POS tagger is used to separate
POS tokens).
4)Opinion words (Opinion words are words which express
positive (good) or negative (bad) emotions) [3].
5)Negations (Negation words (not, not only) shift sentiment

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orientation in a sentence)
6)Syntactic dependency (It is represented as a parse tree and
it contains word dependency based features)
Feature Selection
1)Information gain (based on the presence and absence of a
term in a document a threshold is set and the terms with
less information gain is removed).
2)Odd Ratio (It is suitable for binary class domain where it
has one positive and one negative class for classification.
3)Document Frequency measures the number of appearances
of a term in the available number of documents in the
corpus and based on the threshold computed the terms are
removed. Features weighting mechanism The mechanisms
are of two types. They are 1: Term Presence and Term
Frequency- word which occurs occasionally contains more
information than frequently occurring words. 2: Term
frequency and inverse document frequency (TFIDF) -
Documents are rated where highest rating is given to words
that appear regularly in a few documents and lowest rating
for words that appear regularly in every document. Feature
Reduction Feature reduction reduces the feature vector size
to optimize the performance of a classifier.
Reduction of the number of features in the feature vector can
be done in two different ways in which top n-features can be
left in the vector and either low level or unwanted linguistic
features could be removed. Adjectives only Adjectives have
been used most frequently as features amongst all parts of
speech. A strong correlation between adjectives and
subjectivity has been found. Although all the parts of speech
are important people most commonly used adjectives to
depict most of the sentiments and a high accuracy have been
reported by all the works concentrating on only adjectives for
features generation. Adjective-Adverb Combination Most of
the adverbs have no prior polarity.
But when they occur with sentiment bearing adjectives, they
can play a major role in determining the sentiment of a
sentence. Adverbs alter the sentimental value of the adjective
that they are used with. Adverbs of degree, on the basis of the
extent to which they modify this sentimental value, are
classified as:
 Adverbs of affirmation: certainly, totally
 Adverbs of doubt: maybe, probably
 Strongly intensifying adverbs: exceedingly, immensely
 Weakly intensifying adverbs: barely, slightly
 Negation and minimizers: never Some of the positive
Adjectives are as follows dazzling, brilliant, phenomenal,
excellent and fantastic. Negative Adjectives: suck, terrible,
awful, unwatchable, hideous.
5. Standard Structure of Sentimental Analysis
Opinion Mining also called sentiment analysis is a process of
finding user’s opinion towards a topic or a product. Opinion
mining concludes whether the user’s view is positive, minus,
or neutral about a product, issue, event, etc. Opinion mining
and summarization process involve three primary steps, first
is Opinion Retrieval, Opinion Classification and Opinion
Summarization. Review Text is retrieved from review
websites. Opinion text in blog, reviews, comments, etc.
contains subjective information about the topic.
Reviews classified as positive or negative review. Opinion
summary is generated based on features opinion sentences by
considering frequent features about a matter.
5.1 Opinion Retrieval
It is the procedure of collecting review text from review sites.
Different review websites contain reviews for products,
movies, hotels and news.
5.2 Information retrieval
Techniques such as web crawler can be employed to collect
the review text data from many sources and store them in a
database. This step involves retrieval of reviews, micro-blogs
and comments by user.
5.3 Opinion Classification
Primary steps in sentiment analysis are a classification of
review text. Given a review document M = {M1….. M1} and
a predefined category set K = {positive, negative}, sentiment
classification is to classify each day in M, with a label
expressed in K. The approach involves classifying review
text into two forms namely positive and negative [9].
Machine learning and dictionary based approach is more
popular [3].
5.4 Opinion Summarization
Summarization of opinion is a major character in the opinion
mining process. Summary of reviews provided should be
based on features or subtopics that are mentioned in the
reviews. Many works have been done on summarization of
product reviews [9].
The opinion summarization process mainly involves the
following two approaches. Feature based summarization a
type summarization involves the finding of frequent terms
(features) that are appearing in many reviews. The summary
is submitted by selecting sentences that contain particular
feature information. Characteristics present in review text can
be identified using Latent Semantic Analysis (LSA) method.
Term frequency is a count of term occurrences in a
document. If a term has higher frequency it means that the
condition is more import for summary presentation. In many
product reviews certain product features come out frequently
and associated with user opinions about it. Fig. 3 has the
architecture of Opinion Mining which says how the input is
being classified on the various steps to summarize the
reviews.

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Figure 3: Architecture of opinion mining
6. Hierarchy of Opinion Mining
 Document level Opinion Mining- A single document of
opinionated text works as a basic data unit in this level [7].
Here the document level classification is a single review
about a topic is viewed. Merely in the forums or blog
scenario, there are possibilities for comparative sentences
to appear and for clients to compare one product with
another that has alike characteristics and that’s how
document level analysis is not suitable for forums and web
logs. Therefore the subjectivity/objectivity arrangement is
very vital in this type of classification.
 Sentence level Opinion Mining- The calculated polarity of
each sentence is considered in the case of sentence level
Opinion Mining. The same classification approach as
applied in document level, can be reactive to the sentence
level classification problem also, but Objective and
subjective sentences [12] necessarily be localized. Opinion
words are carried by subjective sentences. These sentiment
words aid to determine the sentiments related to that entity.
After which the polarity classification takes place into
positive and negative classes.
 Phrase level Opinion Mining- This level of classification is
much more pinpointed approach to opinion mining. Here
phrases containing opinion words are observed and the
phrase level class is completed. Only in some special
cases, where contextual polarity also matters, the effect
may not be fully precise.
7. Techniques
Major data mining techniques used to dig the knowledge and
information are: generalization, classification, clustering,
genetic algorithm, association rule mining, data visualization,
neural networks, fuzzy logic, Bayesian networks, and,
decision tree. Number 5 has the techniques of Opinion
Mining. Figure 5. Techniques of Opinion Mining
 Supervised Machine Learning: Classification is most
oftenly used and very popular data mining technique [11].
Classification used to divide the possible results from a
given data set is based on the basis of a defined set of
attributes and a given predictive attributes. The given
dataset is used as the training dataset consist of
independent variables (properties of the dataset) and a
dependent attributes (predicted attribute). A training
dataset created model test on text corpus holds the same
attributes but no predicted attribute. Accuracy of model
checks on how faultless it is making a prediction. Double
Propagation Algorithm is used to extract Product features
and sentenced words.
Figure 4: Techniques of Opinion
 Unsupervised Learning- It differs to supervised learning,
because unsupervised learning does not have definite
targeted output connected with the input. Class label for
any instance is not known so this technique of learning is
about to learn by observation. Clustering is a technique
which is also used in unsupervised learning. Clustering is
an approach of bunching objects with similar properties
into a group. Objects in a cluster are always dissimilar to
the objects in other clusters.
 Case Based Reasoning- Case based reasoning is one of the
emerging Artificial Intelligence supervised techniques.
CBR is a fierce tool of computer reasoning and crack the
problems (cases) in the closest way to real time scenario.
This is a problem solving technique in which knowledge is
personified as past cases in the library and it is not
dependent on classical rules. The solutions of all the cases
are stored in CBR warehouse known as Knowledge base or
Case base.
8. Semantic Orientation
Problem of Opinion mining can be divided into two parts
which are sentiment classification [13] and feature based
opinion mining. The trouble of taking out the semantic
orientation (SO) of a text (i.e., whether the text is positive or
negative towards a peculiar subject matter) often takes as a

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starting point the problem of determining semantic
orientation for individual speech. The hypothesis is that, if
the SO of relevant words in a text is given, SO for the entire
text can be determined. The SO approach to Sentiment
analysis is an unsupervised learning because it does not need
advance training in order to mine the data. Figure 6 shows
the details of the classification of approaches of semantic
orientation.
Figure5: Classification of Approaches of Semantic
Orientation
 Corpus Based Approach- Emotional affinity of words is
determined by Popular corpus-driven method. Emotional
affinity is meant to learn their probabilistic affective scores
from large corpora. The method to assign a happiness
factor to words depending on the frequency of their
occurrences in happy-labelled blog posts compared to their
total frequency in a corpus containing blog posts labelled
with “happy” and “sad” mood annotations. They also
compare the happiness factor scores of words with the
scores in the list.
 Dictionary Based Approach- Dictionary based approach
contains used lexical resources (e.g-Word Net) which work
as an asset to automatically acquire emotion-related words
for emotion classification experiments. They start from a
set of primary emotion adjectives, and then retrieve alike
words from Word Net by utilizing all senses of all words in
the synsets that contain the emotion adjectives. The
process takes advantage of the synonym and hyponym
relations in Word Net to manually find alike words to
nominal emotion words. The affective weights are
automatically acquired from a very large text corpus in an
unsupervised fashion.
9. Tools Used In Opinion Mining
The tools used in the process of tracking the opinion or
polarity from the user’s generated contents are:
 Review Seer tool – Work done by aggregation sites is
automated by this tool. To collect positive and negative
opinions for assigning a score to the extracted feature
terms, the Naive Bayes classifier approach is used. The
results are displayed as a simple opinion sentence [10].
 Web Fountain - Beginning definite Base Noun Phrase
(BNP) heuristic approach is used here for extracting the
product features. Development of a simple web interface is
also possible.
 Red Opal –This tool allows the users to determine the
features based opinion orientations of products. It assigns
the scores to each and every product based on features
extracted from the customer reviews. The results are
displayed by a web based interface [1].
 Opinion observer-This is an opinion mining system which
is used to analyze and compare different opinions [5] on
the cyber space by using user generated contents. This
system illustrates the results in a graph format clearly
showing opinion of the product feature by feature. It uses a
WordNet Exploring method to assign prior polarity.
10. Conclusion
Opinion mining is an emerging sphere of data mining used to
receive the knowledge of the huge mass of data (data may be
customer comments, feedback and reviews on whatever
product or topic etc). Much research has been carried on to
mine the opinions in the contour of a document, sentence and
feature level sentiment analysis. It has been examined that
now the opinion mining trend is proceeding to the
sentimental reviews of twitter data, comments used in
Facebook on pictures, videos or Facebook status. Therefore,
this paper discusses about an overview of the sentimental
analysis approach of Opinion Mining in detail with the
techniques and tools.
References
[1] Ayesha Rashid et al, “A Survey Paper: Areas,
Techniques and Challenges of Opinion Mining”,
International Journal of Computer Science (IJCSI), Vol
10 Issue 6 No 2, Nov 2013.
[2] David Osimo and Francesco Mureddu, “Research
Challenge on Opinion Mining and Sentiment Analysis”.
[3] Bluesy Selvam, A. Abirami, “A Survey on Opinion
Mining Framework”, International Journal of Advanced
Research in Computer and Communication
Engineering,Vol 2, Issue 9, Sep 2013Pg No 3544-3549.
[4] Dongjoo Lee et al, “Opinion Mining of Customer
Feedback Data on the Web”. Seoul National University.
[5] S. Chandrakala, C. Sindhu, “Opinion Mining and
Sentiment Classification: A Survey”,ICTACT Journal on
Soft Computing, Oct 2012 Vol 3 Issue 1,Pg No 420-425.
[6] S.Padmaja et al, “Opinion Mining and Sentiment
Analysis – An Assessment of Peoples’ Belief: A
Survey”, International Journal of Ad hoc, Sensor &
Ubiquitous Computing IJASUC, Vol 4 No 1, Feb 2013.
[7] Raisa Varghese, Jayasree, “A Survey on Sentiment
Analysis and Opinion Mining”, International Journal of
Research in Engineering and Technology (IJRET), Vol 2
Issue 11 Nov 2013.
[8] Sindhu, Chandrakala, “A Survey on Opinion Mining and
Sentiment Polarity Classification”, International Journal
of Emerging Technology and Advanced Engineering.Vol
3 Issue 1, Jan 2013.
[9] Vijay. B. Roth et al, “Survey on Opinion Mining and
Summarization of User Reviews on Web”, International
Journal of Computer Science and Information
Technologies (IJCSIT),Vol 5(2), 2014. 1026-1030.
[10]G. Vinodhini et al, “Sentiment Analysis and Opinion
Mining: A Survey”, International Journal of Advanced
Research in Computer Science and Software
Engineering (IJARCSSE), Vol 2, Issue 6, June 2012.

www.ijsr.net
[11]Arti Buche, Dr.M.B.Chandak, Akshay Zadgoanakar
“Opinion Mining and Analysis: A Survey”, International
Journal on Natural Language Computing (IJNLC) Vol 2
No 3 June 2013Pg No 39-48.
[12]Nidhi Mishra et al, “Classification of Opinion Mining
Techniques”, International Journal of Computer
Applications, Vol 56, No 13, Oct 2012Pg No 1-6.
[13]Nilesh M. Shrike et al, “Survey of Techniques for
Opinion Mining”, International Journal of Computer
Applications, Vol 57 No 13. Nov 2012Pg No 30-35.
[14]Dr. Ritu Sindhu, Ravendra Ratan Singh Jandail, Rakesh
Ranjan Kumar, “A Novel Approach for Sentiment
Analysis and Opinion Mining”, International Journal of
Emerging Technology and Advanced Engineering
(IJETAE), Vol 4, Issue 4, April 2014.
[15]Bakhtawar Seerat, Farouque Azam, “Opinion Mining:
Issues and Challenges (A Survey)”, International Journal
of Computer Applications, Vol49 No 9 July 2012Pg No
42-51.
[16]Rudy Prabowo, Mike Thelwell, "Sentiment Analysis: A
Combined Approach".
[17]Bo Pang, Lillian Lee, "A Sentimental Education:
Sentiment Analysis Using Subjectivity Summarization
Based on Minimum Cuts".
[18]Alec Go, Richa Bhayani, Lei Huang, "Twitter Sentiment
Classification Using Distant Supervision".
[19]Archana Shukla, "Sentiment Analtsis of Document
Based on Annotation".
[20]Meena Rambocas, Joao Gama, "Marketing Research:
The Role of Sentiment Analysis".
Author Profiles
Abhishek Kaushik is currently working in Siemens,
Germany as a Master thesis student. He is in the final
phase of completing his Masters degree in Information
Technology from Kiel University of Applied Sciences.
Before starting his Masters he received his Bachelor’s of
Technology in Computer Science Engineering from Kurukshetra
University in 2012.
Anchal Kaushik is pursuing her MBA in Competitive
Intelligence and Strategy Management from Amity
University Noida. Before this she received her
Bachelor's of Commerce Degree in 2014 from CCS
Meerut
Sudhanshu Naithani has received his Bachelor's of
Technology in Computer Science Engineering from
Kurukshetra University in 2015. He is currently
working as a research assistant under Assistant
Professor Ravinder Madan at Manav Bharti University,
Solan.

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