Presentation on Sentiment Analysis

It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.

1. Sentiment Analysis Presented By- Rebecca Williams

2. Overview: Abstract Introduction What is Sentiment Analysis ? Applications & uses Advantages Step by Step process of SA Simple Example using TextBlob

3. Abstract Triple talaq is also known as talaq-e-biddat instant divorce. It is a kind of Islamic divorce used by Muslims in India. It allows Muslims man to divorce their wife legally by simply stating the word ‘Talaq' three times in any form which can be in any way (verbal, written, or in electronic form). Now a day, the huge amount of data is posted on daily basis on the social media platform. Twitter is a well known social networking platform where the user can post their views, opinions, and thoughts freely. The sentimental analysis is a process of understanding opinions, thoughts and feelings of people about a given subject. This paper analyses tweets posted on Twitter on the subject Triple from the year 2002 to the year 2019. We have transformed unstructured data into well-informed data for getting the insights of people. The main focus of the work is to analyze the feelings of people using two well-known API like TextBlob, and SpaCy. These APIs are based on Lexicon approach. This paper predicts sentiment into three classes positive, negative and neutral.

4. Introduction In this paper, we are applying statistics, natural language processing (NLP), and machine learning to identify, analyze and extract some important information from tweets. The main objective is to observe the reviewer’s feelings, expressions, thoughts or judgments about Triple Talak. Sentiment Analysis can be done by either machine learning or lexicon-based approach. In this paper, we have applied a Lexicon based approach. This is a feasible and practical approach which can analyze tweet text without training or using machine learning. Lexicon is a collection of words or one can say it is like a dictionary in which words are arranged alphabetically. This approach is subdivided into a dictionary-based approach and corpus-based approach. Here we are using a corpus-based approach. Corpus is a large body of words or text which formulate a set of conceptual rules that govern a natural language from texts in that language and examine how that language relates to other languages.

5. What does Sentiment Analysis mean? T he process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.

6. Why Sentiment Analysis?

7. Sentimental Analysis can used as follows: Social media monitoring Brand monitoring Voice of customer (VoC) Customer service Workforce analytics and voice of employee Product analytics Market research and analysis

8. Advantages Scalability: Sentiment analysis allows to process data at scale in a efficient and cost-effective way. Real-time analysis: A sentiment analysis system can help you immediately identify these kinds of situations and take action. Consistent criteria: By using a centralized sentiment analysis system, companies can apply the same criteria to all of their data. This helps to reduce errors and improve data consistency.

9. What is the use of NLP in Sentiment analysis? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes and categories within a sentence or phrase. Natural Language Processing (NLP) is a branch of AI that helps computers to understand, interpret and manipulate human language.

10. Sentimental Analysis : Step by Step Process

11. Step 1: Tokenization Tokenization is the process by which big quantity of text is divided into smaller parts called tokens.

12. Step 2: Cleaning the data Remove numbers Stemming/ lemmatization Part of speech tagging Remove punctuation Lowercase

13. Step 3 : Removing the stop words One of the major forms of pre-processing is to filter out useless data. In natural language processing, useless words (data), are referred to as stop words.

14. Step 4: Classification Rule-based systems that perform sentiment analysis based on a set of manually crafted rules. Automatic systems that rely on machine learning techniques to learn from data. Hybrid systems that combine both rule based and automatic approaches.

15. Step 5: Apply Supervised Algorithm for Classification

16. Step 6: Calculation

17. How to classify Sentiment?

18. Machine Learning/Automatic This approach, employes a machine-learning technique and diverse features to construct a classifier that can identify text that expresses sentiment. Nowadays, deep-learning methods are popular because they fit on data learning representations. Lexicon-Based/Rule-based This method uses a variety of words annotated by polarity score, to decide the general assessment score of a given content. The strongest asset of this technique is that it does not require any training data, while its weakest point is that a large number of words and expressions are not included in sentiment lexicons. Hybrid The combination of machine learning and lexicon-based approaches to address Sentiment Analysis is called Hybrid. Though not commonly used, this method usually produces more promising results than the approaches mentioned above.

19. Algorithms used : There are three machine learning classification algorithms that are predominantly used for sentiment analysis: Support Vector Machines (SVMs) Naive-bayes Decision Trees Each has its own advantages and drawbacks; however, a few different studies have concluded that the Naive-Bayes classifier is the more accurate of the three. There are also two main algorithms used within a lexicon based approach: Corpus Dictionary The most accurate and best approach is a combination of both. However, today we’ll go into one of the more widely used machine learning algorithms which is the Naive-Bayes algorithm.

20. Let’s see a simple example :

21. What is TextBlob? TextBlob is a python library and offers a simple API to access its methods and perform basic NLP tasks. The sentiment function of textblob returns two properties, polarity, and subjectivity. Polarity is float which lies in the range of [-1,1] where 1 means positive statement and -1 means a negative statement. Subjective sentences generally refer to personal opinion, emotion or judgment whereas objective refers to factual information. Subjectivity is also a float which lies in the range of [0,1].

22. Code example:- from textblob import TextBlob Feedback1 ="unbelievably disappointing" Feedback2 ="Terrible pitching and awful hitting led to another crushing loss." Feedback3 ="this is the greatest screwball comedy ever filmed" Feedback4 ="It was pathetic.The worst part about it was the boxing scenes." blob1= TextBlob(Feedback1) print(blob1.sentiment) blob2= TextBlob(Feedback2) print(blob2.sentiment) blob3= TextBlob(Feedback3) print(blob3.sentiment) blob4= TextBlob(Feedback4) print(blob4.sentiment)

23. Output Sentiment(polarity=-0.6, subjectivity=0.7) Sentiment(polarity=-0.5333333333333333, subjectivity=0.9666666666666667) Sentiment(polarity=1.0, subjectivity=1.0) Sentiment(polarity=-1.0, subjectivity=1.0)

24. “ Just as knowledge makes human intelligent, data makes software intelligent.” - Amarpreet Kalkat, Frrole

25. Any Questions???

26. Thank you