This document discusses sentiment analysis, which analyzes texts like reviews and blogs to determine the attitude or opinion expressed. It goes through the phases of preprocessing data, extracting key features, and classifying the sentiment. The purposes are to help customers and organizations by analyzing feedback data. Limitations include not providing alternative suggestions, and an inability to formulate a new combined algorithm. Algorithms used are Naive Bayes, fuzzy conventional method, and hidden Markov models.

1. Sentimental
Analysis
SUBMITTED BY:
ANKIT KHERA(9911103431)

2. An Overview
 Sentiment analysis is a part of Natural Language Processing where
attitudes or opinions of user are undermined
 The texts from reviews or Blogs is processed to get an accurate
description of how the writer feels regarding the subject.

3. Phases
 Pre processing phase: the data entered by user is first cleaned to
reduce noise so that key words could be analyzed
 Feature Extraction Phase: In this phase the key words are given a
token and are put under analysis
 Classification Phase: Now we have all the data waiting to be
classified. Based on different algorithms they are put under
Category of happy, sad etc.

4. Purpose
 With many websites enabling review option for products, there is an
incremental increase in data in natural language.
 Analysis of this data is beneficial for both Customers as well as
organizations to keep track of product Activity.
 Data from feedback forms can be processed to find sentiment of
people regarding the teacher/subject/college.

5. Limitation
 This project does not provide alternate suggestions for the products
to which customers are not satisfied with.
 A combined new algorithm could not be formulated to put forward
the best way to evaluate in future.

6. Algos Used
 Naïve Bayes(Conventional) Theorem: This is the conventional
theorem that collects term frequency and classify data based on
that information.
 Fuzzy Conventional Method: It provides a degree of associativity to
each key word with respect to whole document.
 Hidden Markov Model: This is an algorithm used in predictive
analysis. Based on current mood it can predict what the probability
of future sentiment could be.

7. Screenshots