PRODUCT SENTIMENT ANALYSIS

-ByR.NANCYAMALA,

J.NIVEDHA,

BIG DATA

BIG DATA – is a general term used to describe the voluminous amount of unstructured and semi-structured data.

Big data analytics is the process of examining large amounts of data of a variety of types.

It can process up to 1 yottabytes of data (10008 bytes).

It is basically a concept not technology.

SENTIMENT ANALYSIS

Sentiment data is unstructured data that represents opinions,

emotions, and attributes contained in sources such as - social media posts.- blogs.- online product reviews. - customer support interactions.

Sentiment analysis aims to determine the attitude or the emotional communication of the public with respect to some topic.

SENTIMENT ANALYSIS IN BIG DATA

The ultimate dream of many Big Data users is to tame the vast amount of unstructured data in social media, harnessing how people are thinking, talking, and feeling about a product.

Sentiment analysis scans tweets, pins, likes, and more for evidence of good, bad, or even indifferent impressions.

SOFTWARES USED

Sentiment analysis is implemented using

Apache hadoop – A software that is required to analyze huge . amount of data.

Apache flume – It is required to fetch the data from a social . site to HDFS.

Rstudio – It enables us to present the analyzed data in a graphical manner.

OVERALL PROCESS

HOW THE DATA IS OBTAINED..?

In this analysis sentiment data related to the product ‘Google glass’ is obtained from a social site Twitter using Flume.

HOW DATA IS PROCESSED…?

The obtained data is analyzed using map/reduce technique in hadoop environment.

ANALYZED DATA…

The sentiments obtained is represented in graph in hourly basis using Rstudio.

BENEFITS OF PRODUCT SENTIMENT ANALYSIS

Companies come to know their online brand reputation in market.

Gain Business Intelligence.

Improve Customer Service.

Guides the customers to choose a product.

RESEARCH WORKS

Noisy texts (those with spelling/grammatical mistakes, missing/problematic punctuation and slang) are still a big challenge to most sentiment analysis systems.

Many of the statements about entities are factual in nature and yet they still carry sentiment. Current sentiment analysis approaches determine the sentiment of subjective statements and overlook such objective statements.