just and introduction of sentiment analysis

1. BY RAKA BANERJEE,
HEMANTH VADDE &
ARNAB GAYEN

2. Data mining is the computational process of
discovering patterns in large data sets involving
methods at the intersection of artificial intelligence,
machine learning, statistics, and database systems.
It is an interdisciplinary subfield of computer
science. The overall goal of the data mining
process is to extract information from a data set
and transform it into an understandable structure
for further use.

3.  Aside from the raw analysis step, it involves
database and data management aspects, data pre-
processing, model and inference considerations,
interestingness metrics, complexity considerations,
post-processing of discovered structures,
visualization, and online updating.
 The actual data mining task is the automatic or
semi-automatic analysis of large quantities of data
to extract previously unknown, interesting patterns
such as groups of data records, unusual record,
and dependencies.

4. Text mining, also referred to as text data mining,
roughly equivalent to text analytics, this is the
process of deriving high-quality information from
text. High-quality information is typically derived
through the devising of patterns and trends
through means such as statistical pattern learning.
Text Mining is understood as a process of
automatically extracting meaningful, useful,
previously unknown and ultimately comprehensible
information from textual document repositories.
Text Mining = Data Mining (applied to text data) + Basic Linguistics

5.  Text mining usually involves the process of
structuring the input text deriving patterns within
the structured data, and finally evaluation and
interpretation of the output.
 Text analysis involves information retrieval to study
word frequency distributions, pattern recognition,
tagging, information extraction, data mining
techniques including link and association analysis,
visualization and predictive analytics.
 The overarching goal is, essentially, to turn text
into data for analysis, via application of natural
language processing (NLP) and analytical methods.

6. Sentiment analysis or opinion mining is the
computational study of people’s opinion’s,
sentiments, attitudes and emotions expressed in
written language. Also it refers to the task of
natural language processing to determine whether
a piece of text containing some subjective
information.

7.  Sentiment analysis (sometimes known as opinion
mining or emotion AI) refers to the use of natural
language processing, text analysis, computational
linguistics, and biometrics to systematically
identify, extract, quantify, and study affective
states and subjective information.
 This analysis is widely applied to voice of the
customer materials such as reviews and survey
responses, online and social media, and healthcare
materials for applications that range from
marketing to customer service to clinical medicine.

8.  Improve customer service (taste and
preference).
 Review of brands which are trending in the
market.
 Beat the competition.
 Gain business intelligence.
 Public opinion on an topic/issue.

9.  Application to review or related to websites.
-movie reviewer,product reviewer,poll prediction.
 Application in business and govt intelligence.
-knowing consumer attitudes and trends
 Application across different domains.
-knowing public opinions for political leaders or
there notions about rules and regulations in place
etc.
 Application as a sub-component technology.
-detecting antagonistic,heated language in mails.
-spam detection, context sensitive information
detection, etc.

10. Here we used R-Programming language for
performing Sentimental Analysis.
The packages that has been used are:
 ggplot2
 tm
 syuzhet
 twitteR
 Rcurl
 wordcloud

11. Read Data File
Build Corpus & Clean the Text
Term Document Matrix
Plots
Analyze