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International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
35 Mukesh Yadav, Varunakshi Bhojane
Data Analysis & Sentiment Analysis for Unstructured Data
Mukesh Yadav, PIIT NEW PANVEL
Varunakshi Bhojane, PIIT NEW PANVEL
ABSTRACT
Data analysis means analyzing the information in order to draw the conclusion and understand the
overall situation. In order to understand data machine needs to understand what are sentiments from
the input like movie review, news review, product review, comments from blogs or posts or any
other social website and give output as positive or negative review. Various algorithms and
classifiers are present for sentiment analysis. This paper gives a survey of various classifiers and the
approaches used.
Keywords Data analysis, Qualitative data, Quantitative data, Sentiment, Sentiment Analysis, Natural Language
Processing.
INTRODUCTION Data analysis means analyzing information in ways that reveal relationships, patterns, trends found
within it. It tells us to what level we can trust the answers we are getting by comparing our
information with others to get drawing the conclusion from the data. There are two kinds of data to
work with. One is Qualitative data which refers to the information which is collected or can be
translated into numbers, which can be displayed and analyzed mathematically. Another is Qualitative
data which are collected as descriptions, anecdotes, opinions, quotes, interpretations, etc. Data
analysis popular tool used is excel in which we can sort in ascending and descending order, filter our
data that meet certain criteria, conditional formatting to highlight cells, display charts, extract the
significance from a large and detailed data set using pivot tables, tables to analyze our data quickly
and easily, Solver that uses techniques from the operations research to find optimal solutions for all
kinds of decision problems, Analysis ToolPak which is an add-in program that provides data analysis
tools for financial, statistical and engineering data analysis.
Text information contains facts and opinions. Facts are entities, events and their properties also called
as objective expressions. Opinions describe a person’s feelings, sentiments, appraisal’s towards
entities, events and their properties. Sentiments are important and crucial whenever we need to make a
decision we want to know other’s opinions. Whether the decision made or yet to be made is matching
the opinions of the others in order to avoid self loss. One of the reasons for the lack of opinions is the
fact that there was little opinionated text available before the World Wide Web. Before the web, when
a person needed to make a decision, he or she asks for opinion from friends and family. But the web
changed the way that people express their views and opinions by posting reviews of products at
merchant sites, on Internet forums, discussion groups and blogs which are collectively called as the
user-generated content. This online word of mouth behavior represents new and measurable sources
of information with many practical applications. Sentiment analysis is the name given to the process
of identifying the people’s attitudes and emotional states from language. In Natural Language
Processing (NLP), sentiment analysis is an automated task where machine learning is used to rapidly
determine the sentiment of large amount of text or speech. Applications include tasks like determining
how excited someone is about an upcoming movie, political party with people’s likeliness to vote for
that party, converting written restaurant review into 5-star scale across various categories like food
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
36 Mukesh Yadav, Varunakshi Bhojane
quality, service and value of money. Automated sentiment analysis is the process of training a
computer to identify sentiment within content through Natural Language Processing (NLP).
Automated sentiment analysis will never be as accurate as human analysis, because it doesn’t account
for subtleties of sarcasm or body language.[1]
LITERATURE SURVEY While most researchers focus on assigning sentiments to documents others focus on more specific
tasks like finding the sentiments of words (Hatzivassiloglou & McKeown 1997), subjective
expressions (Wilson et al. 2005; Kim & Hovy 2004), subjective sentences (Pang & Lee 2004) and
topics (Yi et al. 2003; Nasukawa & Yi 2003; Hiroshi et al. 2004). These tasks analyse sentiment at a
fine grained level and can be used to improve the effectiveness of a sentiment classification, as
shown in Pang & Lee (2004). Instead of carrying out a sentiment classification or an opinion
extraction, Choi et al. (2005) focus on extracting the sources of opinions, e.g., the persons or
organizations who play a crucial role in influencing other individuals’ opinions. Various data sources
have been used, ranging from product reviews, customer feedback, the Document Understanding
Conference (DUC) corpus, the Multi Perspective Question Answering (MPQA) corpus and the Wall
Street Journal (WSJ) corpus. To automate sentiment analysis, different approaches have been applied
to predict the sentiments of words, e pressions or documents. hese are atural anguage
rocessing and pattern-based i et al. asukawa i iroshi et al. onig
Brill 2006), machine learning algorithms, such as Naive Bayes (NB), Maximum Entropy (ME),
Support Vector Machine (SVM) (Joachims 1998), and unsupervised learning (Turney 2002).
Table 1. Summary of Literature Survey
Sr.
No.
Title of the paper Author & Year of
publication
Observations/Remarks
1. Thumbs up? Sentiment classification
using machine learning techniques
Bo Pang, Lillian Lee,
and Shivakumar
Vaithyanathan, 2002
This paper used Naïve based classification, Maximum entropy
and support vector machine. N-gram approach along with POS
information is used to perform machine learning for determining
the polarity. Tried different variations of n-gram, unigram,
bigram, position and POS. SVM gave the highest accuracy with
unigram feature. Accuracy 77-82.9
2. Thumbs up or thumbs down? Semantic
orientation applied to unsupervised
classification of reviews
Peter D. Turney, 2002 This paper used tag patterns with a window of maximum 3
words. In his experiment adjective(JJ),adverb(RB), single
common noun(NN), plural common noun (NNS) were
considered. Given a phrase, PMI (Point-wise Mutual
Information) is calculated. Algorithm follows 3 steps: extract
phrases containing adjective or adverbs, calculate semantic
orientation & average semantic orientation. Accuracy of 65.8-84
3. Sentiment analysis: capturing
favorability using natural language
processing
Tetsuya Nasukawa
and Jeonghee Yi,2003
Objective is to assign topic (subject term & paragraph)
sentiments. It uses NLP and pattern based model. Data source
taken were web pages & camera reviews. POS tagging is used to
disambiguate some expressions. Syntactic parsing is used to
identify relationships between sentiment expressions and subject
term. Data set is present. Accuracy of 94.3 &94.5, Recall value
28.6 & 24.
4. Determining the Sentiment of opinions SOO Kim and E.
Hovy, 2004
Describe an opinion as a quadruple [Topic, Holder, Claim,
Sentiment] in which holder believes a Claim about the topic & in
many cases associates with the sentiment. The system operates in
4 steps-select sentence that contain both topic & holder
candidates, holder based regions of opinions are delimited,
sentence sentiment classifier calculates polarity of all sentiment
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
37 Mukesh Yadav, Varunakshi Bhojane
bearing words, system combines them to produce holders
sentiment for the whole sentence. It uses probabilistic based
model. DUC 2001 corpus is used. Accuracy between 75.6-81
5. A sentimental education: Sentiment
analysis using subjectivity
summarization based on minimum cuts
Pang, Bo, and Lillian
Lee, 2004
Uses categorization technique only for the subjective portions of
the document. Foe extracting the subjective portions an efficient
technique is used for finding minimum cuts in graphs. It is a 2
step process: one is labeling the sentences in the document as
either subjective or objective, discarding the objective sentences.
Second, applying the standard machine learning classifier to the
resulting extract of subjective sentences.
6. Sentiment analysis: Adjectives and ad-
verbs are better than adjectives alone
Farah Benamara,
Carmine Cesarano,
Antonio Picariello,
Diego Reforgiato, and
V. S. Subrahmanian,
2007
AAC (Adverb-adjective combination) based SA technique is
used. Minimizers are present which are small number of adverbs
such as “hardly” that actually has a negative effect on sentiment.
Unary AAC & Binary AAC scoring function is used to take
input. There are 3 AAC scoring algorithms - Variable Scoring,
Adjective Priority Scoring & Adverb First Scoring. Algorithm
for scoring the strength of sentiment on a topic.
7. Towards Enhanced Opinion
Classification using NLP Techniques
Bakliwal, Akshat, et
al., 2011
It uses 2 approaches. First, Simple N-Gram matching. Second,
POS tagging N-Gram matching. A scoring function which gives
the priority to trigram matching followed by bigrams and
unigrams is proposed. Normalization is done between 0 to 1
scale.
8. Sentiment classification based on
supervised latent n-gram analysis
Bespalov, Dmitriy, et
al., 2011
It proposed an efficient embedding for modeling higher-order (n-
gram) phrases that projects the n-grams to low-dimensional latent
semantic space, where a classification function can be defined.
They utilize a deep neural network to build a unified
discriminative framework to estimate parameters of the latent
space & classification function. Framework is applied to large-
scale sentimental classification task. There are two large data sets
for online product reviews.
9. A system for real time Twitter
sentiment analysis of 2012 US
Presidential election cycle
Hao Wang et al, 2012 System architecture for real time processing data is given. In
which real time data is taken, preprocessing i.e. tokenization,
Match tweet to candidates, Sentiment model, Aggregate by
candidate, Visualization and Online human annotation i.e.
interface.
10. Large-Scale Sentiment Anlaysis for
News and blogs
Namrata Godbole,
Manjunath
Srinivasaiah, Steven
Skiena, 2007
A system is designed that assign scores indicating positive or
negative opinion to each distinct entity in the text corpus. System
consists of a sentiment classification phase, which associates
expressed opinions with each relevant entity and a sentiment
aggregation and scoring phase, which scores each entity relative
to others in the same class.
PROBLEM STATEMENT
It is normal for us to consult our dear ones whenever we plan to do something significant, for
instance buying a home, going for higher education or choosing a profession. Getting opinions from
other people aids us in decision making. Opinions are important when it comes to making a stable
decision. Opinions about something makes the hold of the claim stronger thus allowing us to make a
suitable and better decision. Sentiment analysis and opinion classification play an important role in
predicting people’s views. The current trends in SA focus on assigning a polarity to subjective
expressions in order to decide the objectivity/subjectivity orientation of a document or the
positive/negative polarity of an opinion sentence within a document. Additional work has focused on
the strength of an opinion expression where each clause within a sentence can have a neutral, low,
medium or a high strength. Most of the work in the past on sentiment analysis deals with determining
the strength of subjective expression within a sentence or a document using the parts of speech.
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
38 Mukesh Yadav, Varunakshi Bhojane
Sentiment analysis tries to classify opinion sentences in a document on the basis of their polarity as
positive or negative, which can be used in various ways and in many applications for example,
marketing and contextual advertising, suggestion systems based on the user likes and ratings,
recommendation systems etc. In the past machine learning techniques have been used to classify
sentiments into positive and negative classes depending on their polarity.
Benamara et al.[7] used adjective adverb combinations to assign positive and negative scores to
sentiments. They generated three axioms to score adverbs of degree. Their methods give a Pearson
correlation as high as 0.47. This technique gives a high precision and recall than previously
developed algorithms. The axioms can be extended to other categories of adverbs, to study other
syntactic constructions and to study the impact of guidelines.
Bakliwal et al.[8] have used two NLP approaches like simple Ngram matching and POS tagged N-
gram matching to assign polarity to opinions. They used their approach on product and movie
reviews. They achieved a maximum accuracy of 76.3.
Keeping in mind the above two approaches we can develop a new algorithm and achieve better
accuracy. Our goal would be to minimize the human effort required to read a huge content and get a
positive or negative opinion out of it. Using the best suitable approach and different classifiers to
solve the problem of sentiment analysis.
CLASSIFIERS To implement machine learning algorithms on document data, we use the following standard bag-of-
feature framework. Let be a be a predefined set of features that can appear in a
document. E amples include the word “still” or the bigram “really stinks”. et be the number
of times occurs in document Then, each document is represented by the document vector. [2]
= (1)
1. Word Sentiment Classifier
In this we assemble small amount of seed words by hand. Sorted by polarity into two lists i.e.
positive and negative. Add words from WordNet. There are synonyms and antonyms. We assume
synonyms of positive words are mostly positive and antonyms mostly negative. For example, the
positive word “good” has synonym “virtuous, honorable, righteous” and antonyms “evil,
disreputable, unrighteous”. Antonyms of negative words are added to the positive list, and synonyms
to the negative one. We use synonym & antonym because it is much simpler than the corpus. But not
all synonyms and antonyms could be used. This indicates the need to develop a measure of strength
of sentiment polarity to determine how strong a word is positive and how strong it is negative. Given
a word WordNet is used to obtain the synonym set of unseen word to determine how it interacts with
sentimental seed lists. We compute
c (2)
where is a sentiment category (positive or negative), is the unseen word and are the
WordNet synonyms of Problems is that is it difficult to pick one sentiment category without
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
39 Mukesh Yadav, Varunakshi Bhojane
considering conte t. Unigram model is not sufficient. For e ample, “ erm limits really hit a
democracy” says rof. Fenno, the multi meaning word “hit” was used to e press negative point of
view about term limits. If such combinations occur adjacently, we can use bigrams or trigrams in the
seed word list. But it is more difficult to identify the sentiment correctly, if one of the word falls
outside the sentiment region. [5]
2. Sentence Sentiment Classifier
It uses direct matching and opinion holder algorithm for identifying the topic. Near each holder we
identify a region in which sentiments would be considered. Any sentiment outside such a region we
classify as undetermined and is ignored. Sentiment region can be defines in various ways like
Window 1 contain full sentence, window 2 contain words between Holder and Topic, window 3
contain window2 plus or minus 2 words, window 4 contain window2 to the end of sentence. Problem
with this is that in a sentence there may be two different opinions and system determines which is the
closest one. For e ample, “She thinks term limits will give women more opportunities in politics”
expresses a positive opinion about term limits but the absence of adjective, verb and noun sentiment
words prevents a classification.[5]
3. Naïve Bayes
A Bayes classifier is a simple probabilistic classifier based on applying Bayes theorem with strong
independence assumptions. A more descriptive term for the underlying probability model would be
”independent feature model”. In simple terms, a aive Bayes classifier assumes that the presence or
absence of a particular feature of a class is not related to the presence or absence of any other feature.
Depending on the precise nature of the probability model, NB classifiers can be trained very
efficiently in a supervised learning setting. In many practical applications, parameter estimation for
NB models uses the method of maximum likelihood; in other words, one can work with the NB
model without believing in Bayesian probability or using any Bayesian methods. An advantage of
the NB classifier is that it requires a small amount of training data to estimate the parameters
necessary for classification. Because independent variables are assumed, only the variances of the
variables for each class need to be determined and not the entire covariance matrix. One approach to
text classification is to assign to a given document the class = . Naïve Bayes
(NB) classifier is derived by first observing that by Bayes rule,
(3)
where P plays no role in selecting . To estimate the term , Naïve Bayes decomposes it
by assuming the are conditionally independent given ’s class. raining method consist of
relatively estimation of and , using add-one smoothing.[2]
4. Maximum Entropy
Maximum entropy classification (MaxEnt, or ME, for short) is an alternate technique which has
proven effective in a number of natural language processing applications (Berger et al., 1996).
Nigam et al. (1999) show that it sometimes, but not always, outperforms Naive Bayes at standard
text classification. Its estimate of takes the following exponential form:
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
40 Mukesh Yadav, Varunakshi Bhojane
(4)
where is a normalization function. is a feature/class function for feature and class ,
defines as follows :
(5)
For instance, a particular feature/class function might fire and only if the bigram “still hate” appears
and the document’s sentiment is hypothesized to be negative. Importantly, unlike aive Bayes,
MaxEnt makes no assumptions about the relationships between features, and so might potentially
perform better when conditional independence assumptions are not met. [2]
5. Multi-Layer Perceptron (MLP)
Multi-Layer perceptron (MLP) is a feed-forward neural network with one or more layers between
input and output layer. Feed-forward means that data flows in one direction from input to output
layer (forward). It can be implemented in Weka toolkit. [8]
6. Bagging
Bagging is a bootstrap ensemble method which creates individuals for its ensemble by training each
classifier on a random redistribution of the training set. Each classifier’s training set is generated by
randomly drawing, with replacement, N examples where N is the size of the original training set;
many of the original examples may be repeated in the resulting training set while others may be left
out. Each individual classifier in the ensemble is generated with a different random sampling of the
training set.
This is a classifier that involves adaptive reweighting and combining to improve classification. This
algorithm uses multiple sets for training. Each bootstrap set is used to train a different component
classifier. The final classification decision is based on vote of each component classifier. The
component classifiers are all of the same form. Certain classifiers become un- stable if there are
small changes in training data, this also gives way to large changes in accuracy. Bagging improves
recognition for unstable classifiers by smoothing over discontinuities.
7. Decision Tree
Decision tree learning uses a decision tree as a predictive model which maps observations about an
item to conclusions about the item’s target value. It is one of the predictive modeling approaches
used in statistics, data mining and machine learning. More descriptive names for such tree models
are classification trees or regression trees. In these tree structures, leaves represent class labels and
branches represent conjunctions of features that lead to those class labels.
In decision analysis, a decision tree can be used to visually and explicitly represent decisions and
decision making. In data mining, a decision tree describes data but not decisions; rather the resulting
classification tree can be an input for decision making.
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
41 Mukesh Yadav, Varunakshi Bhojane
Decision tree learning is a method commonly used in data mining. The goal is to create a model that
predicts the value of a target variable based on several input variables. An example is shown on the
right. Each interior node corresponds to one of the input variables; there are edges to children for
each of the possible values of that input variable. Each leaf represents a value of the target variable
given the values of the input variables represented by the path from the root to the leaf. A decision
tree is a simple representation for classifying examples. Decision tree learning is one of the most
successful techniques for supervised classification learning.
A tree can be ”learned” by splitting the source set into subsets based on an attribute value test. his
process is repeated on each derived subset in a recursive manner called recursive partitioning. The
recursion is completed when the subset at a node has all the same value of the target variable, or
when splitting no longer adds value to the predictions. This process of top down induction of
decision trees is an example of a greedy algorithm, and it is by far the most common strategy for
learning decision trees from data.
A decision tree is a classifier expressed as a recursive partition of the instance space. The decision
tree consists of nodes that form a rooted tree, meaning it is a directed tree with a node called root that
has no incoming edges. All other nodes have exactly one incoming edge. A node with outgoing
edges is called an internal or test node. All other nodes are called leaves (also known as terminal or
decision nodes). In a decision tree, each internal node splits the instance space into two or more sub-
spaces according to a certain discrete function of the input attributes values. In the simplest and most
frequent case, each test considers a single attribute, such that the instance space is partitioned
according to the attributes value. In the case of numeric attributes, the condition refers to a range.
Each leaf is assigned to one class representing the most appropriate target value. Alternatively, the
leaf may hold a probability vector indicating the probability of the target attribute having a certain
value. Instances are classified by navigating them from the root of the tree down to a leaf, according
to the outcome of the tests along the path.
In data mining, decision trees can be described also as the combination of mathematical and
computational techniques to aid the description, categorisation and generalisation of a given set of
data.[9]
8. SVM light
Support vector machines (SVMs) have been shown to be highly effective at traditional text
categorization, generally outperforming Naïve Bayes. They are large-margins, rather than
probabilistic, classifiers, in contrast, to Naïve Bayes and Maximum Entropy. The basic idea behind
the training procedure is to find a hyperplane, represented by vector that not only separates the
document vectors in one class from those in the other, but for which the separation, or margins, is as
large as possible. This search corresponds to a constrained optimization problem; letting
(corresponding to positive and negative) be the correct class of document , the solution
can be written as
:=
, 0 (6)
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
42 Mukesh Yadav, Varunakshi Bhojane
where the ’s are obtained by solving a dual optimization problem. hose vector such that is
greater than zero are called , since they are the only document vectors contributing
to . So SVM
light package is used for training and testing, with all parameters set to their default
values, after first length normalizing the document vectors. [2]
SVM light is an implementation of Vapnik’s Support Vector Machine for the problem of pattern
recognition, for the problem of regression, and for the problem of learning a ranking function.[12]
The algorithm has scalable memory requirements and can handle problems with many thousands of
support vectors efficiently. The software also provides methods for assessing the generalization
performance efficiently. It includes two efficient estimation methods for both error rate and
precision/recall. XiAlpha estimates can be computed at essentially no computational expense, but
they are conservatively biased. Almost unbiased estimates provide leave-one-out testing. SVMlight
exploits that the results of most leave-one-outs are predetermined and need not be computed. New in
this version is an algorithm for learning ranking functions. The goal is to learn a function from
preference examples, so that it orders a new set of objects as accurately as possible. Such ranking
problems naturally occur in applications like search engines and recommender systems. Furthermore,
this version includes an algorithm for training large-scale transductive SVMs. The algorithm
proceeds by solving a sequence of optimization problems lower-bounding the solution using a form
of local search. A similar transductive learner, which can be thought of as a transductive version of
k-Nearest Neighbor is the Spectral Graph Transducer. SVMlight can also train SVMs with cost
models. The code has been used on a large range of problems, including text classification, image
recognition tasks, bioinformatics and medical applications. Many tasks have the property of sparse
instance vectors. This implementation makes use of this property which leads to a Very compact and
efficient representation. SVM light consists of a learning module and a classification module. The
classification module can be used to apply the learned model to new examples. There are different
options for choosing the kernel. Some kernels available are: linear (default), polynomial, radial basis
function, sigmoid.
9. POS Tagger
A Part-Of-Speech Tagger (POS Tagger) is a software that reads text in a language and assigns parts
of speech to each word such as adjective, verb, noun etc. although generally computational
applications use more fine-grained OS tags like ’noun-plural’. his software is a Java
implementation of the log-linear part-of-speech taggers described in Toutanova et al.(2003) The
tagger was originally written by Kristina Toutanova. Since that time, Dan Klein, Christopher
Manning, William Morgan, Anna Rafferty, Michel Galley, and John Bauer have improved its speed,
performance, usability, and support for other languages. There are several versions of POS tagger
available for download. The basic download contains two trained tagger models for English. The full
download contains three trained English tagger models, an Arabic tagger model, a Chinese tagger
model, and a German tagger model. Both versions include the same source and other required files.
The tagger can be retrained on any language, given POS-annotated training text for the language.
The English taggers use the Penn Treebank tag set. The tagger is licensed under the GNU General
Public License (v2 or later). Source is included. Source is included. The package includes
components for command-line invocation, running as a server, and a Java API. The tagger code is
dual licensed (in a similar manner to MySQL, etc.). Open source licensing is under the full GPL,
which allows many free uses. [13]
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
43 Mukesh Yadav, Varunakshi Bhojane
APPLICATIONS These are the applications of sentiment analysis.
o In social media monitoring :
VOC to track customer reviews, survey responses, competitors, it is also practical for use
in business analytics and situations in which text needs to be analyzed.
Computing customer satisfaction metrics : We can get an idea of how happy customers
are with your products from the ratio of positive to negative tweets about them.
o Identifying detractors and promoters
It can be used for customer service, by spotting dissatisfaction or problems with products.
It can also be used to find people who are happy with your products or services and their
experiences can be used to promote your products.
o In finance firms/markets
To forecast market movement based on news, blogs and social media sentiment.
To identify the clients with negative sentiment in social media or news and to increase the
margin for transactions with them for default protection.
There are numerous news items, articles, blogs, and tweets about each public company. A
sentiment analysis system can use these various sources to find articles that discuss the
companies and aggregate the sentiment about them as a single score that can be used by
an automated trading system. One such system is The Stock Sonar. This system
(developed by Digital Trowel) shows graphically the daily positive and negative
sentiment about each stock alongside the graph of the price of the stock.
o Reviews of consumer products and services : There are many websites that provide
automated summaries of reviews about products and about their specific aspects. A notable
example of that is “Google roduct Search.”
o Monitoring the reputation of a specific brand on Twitter and/or Facebook : One application
that performs real-time analysis of tweets that contain a given term is tweetfeel.
o Enables campaign managers to track how voters feel about different issues and how they
relate to the speeches and actions of the candidates.
o Applications in business domain Consider a question : “why aren’t customers buying our
products?” or “why aren’t customers visiting our website?” We know the concrete data:
price, specs, competition, etc.
o In politics/political science; Evaluation of public/voters opinions. Views/discussions of
policy. Law/policy making. Sociology; Psychology : investigations or experiments with data
extracted from NL text.
CONCLUSION The experiments carried out on products/movie/news review dataset and obtained results by
researchers showing the accuracy of the different approaches used. We conclude that parts of speech
tagging gave the best result in classification of 76.6 percent as compared to all approach. The
classifier that gave the best accuracy is the SVM light. The accuracy of classification varies
according to different domains. Classifications can be done for more than two classes. It is possible to
make a set of hybrid classifiers. Only one problem which can be faced while using the POS tagger is
International Journal of Engineering Technology, Management and Applied Sciences
www.ijetmas.com December 2014, Volume 2 Issue 7, ISSN 2349-4476
44 Mukesh Yadav, Varunakshi Bhojane
classifying neutral sentences, sentences that contain a negative as well as positive opinion which are
difficult to classify and are misclassified many times. This issue can be taken up for classification.
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