1.1 as products, services, organizations, individuals, issues, events,

1.1  
Introduction

Sentiment analysis is the field of study that analyzes
people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions
towards entities such as products, services, organizations, individuals,
issues, events, topics, and their attributes. Sentiment analysis, which is also
called opinion mining, involves in building a system to collect and examine
opinions about the product made in blog posts, comments, reviews or tweets. The
meaning of opinion itself is still very broad. Sentiment analysis and opinion
mining mainly focuses on opinions which express or imply positive or negative
sentiments

Now there is a huge volume of opinionated data in the
social media on the Web. Without this data, a lot of sentiment analysis would
not have been possible. Not surprisingly, the inception and the rapid growth of
sentiment analysis coincide with those of the social media. In fact, sentiment
analysis is now right at the center of the social media analysis. Hence
sentiment analysis not only has an important impact on NLP, but may also have a
profound impact on management sciences, political science, economics, and
social sciences as they are all affected by people’s opinions.

Opinions are
central to almost all human activities because they are key influencers of our
behaviors. Whenever we need to make a decision, we want to know others’
opinions. In the real world, businesses and organizations always want to find
consumer or public opinions about their products and services. Individual
consumers also want to know the opinions of existing users of a product before
purchasing it, and others’ opinions about political candidates before making a
voting decision in a political election. In the past, when an individual needed
opinions, he/she asked friends and family. When an organization or a business
needed public or consumer opinions, it conducted surveys, opinion polls, and
focus groups. Acquiring public and consumer opinions has long been a huge
business itself for marketing, public relations, and political campaign
companies.

With the
explosive growth of social media (e.g., reviews, forum discussions, blogs,
micro-blogs, Twitter, comments, and postings in social network sites) on the
Web, individuals and organizations are increasingly using the content in these
media for decision making. Nowadays, if one wants to buy a consumer product,
one is no longer limited to asking one’s friends and family for opinions
because there are many user reviews and discussions in public forums on the Web
about the product. For an organization, it may no longer be necessary to
conduct surveys, opinion polls, and focus groups in order to gather public
opinions because there is an abundance of such information publicly available.

In recent
years, it was witnessed that opinionated postings in social media have helped
reshape businesses, and sway public sentiments and emotions, which have
profoundly impacted on our social and political systems. Such postings have
also mobilized masses for political changes such as those happened in some
countries. It has thus become a necessity to collect and study opinions on the
Web. Of course, opinionated documents not only exist on the Web (called
external data), many organizations also have their internal data, e.g.,
customer feedback collected from emails and call centers or results from
surveys conducted by the organizations.

Due to these
analysis work, industrial activities have flourished in recent years. Sentiment
analysis applications have spread to almost every possible domain, from consumer
products, services, healthcare, and financial services to social events and
political elections. A key feature of social media is that it enables anyone
from anywhere in the world to freely express his/her views and opinions without
disclosing his/her true identify and without the fear of undesirable
consequences. These opinions are thus highly valuable. Twitter is a place where
many people express their views in the form of tweets.

In general, sentiment analysis has been investigated
mainly at three levels. In document level the main task is to classify whether
a whole opinion document expresses a positive or negative sentiment. This level
of analysis assumes that each document expresses opinions on a single entity.
In sentence level the main task is to check whether each sentence expressed a
positive, negative, or neutral opinion. This level of analysis is closely
related to subjectivity classification, which distinguishes objective sentences
that express factual information from subjective sentences that express
subjective views and opinion. Document level and the sentence level analyses do
not discover what exactly people liked and did not like. Aspect level performs
finer-grained analysis. Instead of looking at language constructs (documents,
paragraphs, sentences, clauses or phrases), aspect level directly looks at the
opinion itself.

Sentiment analysis played a great role in the area of
researches done by many, there are many methods to carry out sentiment
analysis. Still many analyses are going on to find out better alternatives due
to its importance in this scenario. Some of the methods are Machine learning strategies.
They work by training an algorithm with a training data set before applying it
to the actual data set. Machine learning techniques first trains the algorithm
with some particular inputs with known outputs so that later it can work with new
unknown data. Some of the most renowned works based on machine learning is Support Vector Machine. It is a non-probabilistic
classifier in which a large amount of training set is required. It is done by
classifying points using a (d-1) dimensional hyper plane. SVM finds a hyper
plane with largest possible margin. Support Vector Machines make use of the
concept of decision planes that define decision boundaries. A decision plane is
one that separates between a set of objects having different class membership.

Naïve Bayes Method is also
a most renowned approach in machine learning strategies. It
is a probabilistic classifier and is mainly used when the size of the training
set is less. In machine learning it is
in family of sample probabilistic classifier based on Bayes theorem. The
conditional probability that an event X
occurs given the evidence Y is determined by Bayes rule.   A Maximum Entropy (ME) classifier, or conditional
exponential classifier, also belongs to machine learning strategies, it is parameterized
by a set of weights that are used to combine the joint-features that are
generated from a set of features by an encoding. The encoding maps each pair of
feature set and label to a vector. ME classifiers belong to the set of
classifiers known as the exponential or log-linear classifiers, because they
work by extracting some set of features from the input, combining them linearly
and then using this sum as exponent.

Lexicon Based strategies work on an assumption that
the collective polarity of a sentence or documents is the sum of polarities of
the individual phrases or words. In this strategy we have two most owned
approaches, they are corpus based and dictionary based. The dictionary method is
governed by the use of a dictionary consisting pre-tagged lexicons. The input
text is converted to tokens by the Tokenizer. Every new token encountered is
then matched for the lexicon in the dictionary. If there is a positive match,
the score is added to the total pool of score for the input text. For instance,
if “dramatic” is a positive match in the dictionary then the total score of the
text is incremented. Other-wise the score is decremented or the word is tagged
as negative. Though this technique appears to be amateur in nature, its
variants have proved to be worthy.

The computational speed and efficiency of
dictionary-based approaches to sentiment analysis, together with their
intuitive appeal, make such approaches an attractive alternative for extracting
emotional context text. At the same time in both dictionaries
based and corpus based approaches re-constructed dictionaries for use with
modern standard U.S. English have the advantage of being exceptionally easy to
use and extensively validated, making them strong contenders for applications
where the emotional content of the language under study is expressed in conventional
ways.1.1  
Introduction

Sentiment analysis is the field of study that analyzes
people’s opinions, sentiments, evaluations, appraisals, attitudes, and emotions
towards entities such as products, services, organizations, individuals,
issues, events, topics, and their attributes. Sentiment analysis, which is also
called opinion mining, involves in building a system to collect and examine
opinions about the product made in blog posts, comments, reviews or tweets. The
meaning of opinion itself is still very broad. Sentiment analysis and opinion
mining mainly focuses on opinions which express or imply positive or negative
sentiments

Now there is a huge volume of opinionated data in the
social media on the Web. Without this data, a lot of sentiment analysis would
not have been possible. Not surprisingly, the inception and the rapid growth of
sentiment analysis coincide with those of the social media. In fact, sentiment
analysis is now right at the center of the social media analysis. Hence
sentiment analysis not only has an important impact on NLP, but may also have a
profound impact on management sciences, political science, economics, and
social sciences as they are all affected by people’s opinions.

Opinions are
central to almost all human activities because they are key influencers of our
behaviors. Whenever we need to make a decision, we want to know others’
opinions. In the real world, businesses and organizations always want to find
consumer or public opinions about their products and services. Individual
consumers also want to know the opinions of existing users of a product before
purchasing it, and others’ opinions about political candidates before making a
voting decision in a political election. In the past, when an individual needed
opinions, he/she asked friends and family. When an organization or a business
needed public or consumer opinions, it conducted surveys, opinion polls, and
focus groups. Acquiring public and consumer opinions has long been a huge
business itself for marketing, public relations, and political campaign
companies.

With the
explosive growth of social media (e.g., reviews, forum discussions, blogs,
micro-blogs, Twitter, comments, and postings in social network sites) on the
Web, individuals and organizations are increasingly using the content in these
media for decision making. Nowadays, if one wants to buy a consumer product,
one is no longer limited to asking one’s friends and family for opinions
because there are many user reviews and discussions in public forums on the Web
about the product. For an organization, it may no longer be necessary to
conduct surveys, opinion polls, and focus groups in order to gather public
opinions because there is an abundance of such information publicly available.

In recent
years, it was witnessed that opinionated postings in social media have helped
reshape businesses, and sway public sentiments and emotions, which have
profoundly impacted on our social and political systems. Such postings have
also mobilized masses for political changes such as those happened in some
countries. It has thus become a necessity to collect and study opinions on the
Web. Of course, opinionated documents not only exist on the Web (called
external data), many organizations also have their internal data, e.g.,
customer feedback collected from emails and call centers or results from
surveys conducted by the organizations.

Due to these
analysis work, industrial activities have flourished in recent years. Sentiment
analysis applications have spread to almost every possible domain, from consumer
products, services, healthcare, and financial services to social events and
political elections. A key feature of social media is that it enables anyone
from anywhere in the world to freely express his/her views and opinions without
disclosing his/her true identify and without the fear of undesirable
consequences. These opinions are thus highly valuable. Twitter is a place where
many people express their views in the form of tweets.

In general, sentiment analysis has been investigated
mainly at three levels. In document level the main task is to classify whether
a whole opinion document expresses a positive or negative sentiment. This level
of analysis assumes that each document expresses opinions on a single entity.
In sentence level the main task is to check whether each sentence expressed a
positive, negative, or neutral opinion. This level of analysis is closely
related to subjectivity classification, which distinguishes objective sentences
that express factual information from subjective sentences that express
subjective views and opinion. Document level and the sentence level analyses do
not discover what exactly people liked and did not like. Aspect level performs
finer-grained analysis. Instead of looking at language constructs (documents,
paragraphs, sentences, clauses or phrases), aspect level directly looks at the
opinion itself.

Sentiment analysis played a great role in the area of
researches done by many, there are many methods to carry out sentiment
analysis. Still many analyses are going on to find out better alternatives due
to its importance in this scenario. Some of the methods are Machine learning strategies.
They work by training an algorithm with a training data set before applying it
to the actual data set. Machine learning techniques first trains the algorithm
with some particular inputs with known outputs so that later it can work with new
unknown data. Some of the most renowned works based on machine learning is Support Vector Machine. It is a non-probabilistic
classifier in which a large amount of training set is required. It is done by
classifying points using a (d-1) dimensional hyper plane. SVM finds a hyper
plane with largest possible margin. Support Vector Machines make use of the
concept of decision planes that define decision boundaries. A decision plane is
one that separates between a set of objects having different class membership.

Naïve Bayes Method is also
a most renowned approach in machine learning strategies. It
is a probabilistic classifier and is mainly used when the size of the training
set is less. In machine learning it is
in family of sample probabilistic classifier based on Bayes theorem. The
conditional probability that an event X
occurs given the evidence Y is determined by Bayes rule.   A Maximum Entropy (ME) classifier, or conditional
exponential classifier, also belongs to machine learning strategies, it is parameterized
by a set of weights that are used to combine the joint-features that are
generated from a set of features by an encoding. The encoding maps each pair of
feature set and label to a vector. ME classifiers belong to the set of
classifiers known as the exponential or log-linear classifiers, because they
work by extracting some set of features from the input, combining them linearly
and then using this sum as exponent.

Lexicon Based strategies work on an assumption that
the collective polarity of a sentence or documents is the sum of polarities of
the individual phrases or words. In this strategy we have two most owned
approaches, they are corpus based and dictionary based. The dictionary method is
governed by the use of a dictionary consisting pre-tagged lexicons. The input
text is converted to tokens by the Tokenizer. Every new token encountered is
then matched for the lexicon in the dictionary. If there is a positive match,
the score is added to the total pool of score for the input text. For instance,
if “dramatic” is a positive match in the dictionary then the total score of the
text is incremented. Other-wise the score is decremented or the word is tagged
as negative. Though this technique appears to be amateur in nature, its
variants have proved to be worthy.

The computational speed and efficiency of
dictionary-based approaches to sentiment analysis, together with their
intuitive appeal, make such approaches an attractive alternative for extracting
emotional context text. At the same time in both dictionaries
based and corpus based approaches re-constructed dictionaries for use with
modern standard U.S. English have the advantage of being exceptionally easy to
use and extensively validated, making them strong contenders for applications
where the emotional content of the language under study is expressed in conventional
ways. 

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