Emotion & Sentiment Analysis with/without NLTK using Python
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- Find out Emotions in a text ( happiness, sadness, jealousy etc. )
- Positive and Negative - Sentiment Analysis
- Scrap Tweets from Twitter and find out the emotion and sentiment of those tweets
- Learn Natural Language Processing Techniques
- Cleaning Text and Data for Language Processing ( NLP )
- Learn to create graphs using Matplotlib and plot the emotions graph
- Learn NLTK for Sentiment Analysis and Natural Language Processing
Welcome to this new video series in which we will be using Natural Language Processing or it's called NLP in short. to analyse emotions and sentiments of given text. After completing this videos series - 1) You will be able to analyse different emotions present in an essay like sadness, happiness, jealousy etc 2) You will be able to find out the dominant emotion in the text 3) You will be able to plot those emotions on a graph 4) And you will also be able to tell whether the whole text is a positive or negative emotion 5) And finally you will also be able scrap tweets with a hashtag and find out the public opinion on that hashtag. For example you can search for #donaldtrump and find out whether that emotion is associated with a positive or a negative sentiment. First we will be doing all the natural language processing and sentiment analysis on our own without the use of a library or a package. So that you guys properly understand the concepts of NLP and then we can go on to use NLTK library to shorten our work.
All right guys! Welcome back. In this video we are going to learn how to clean the text before we can apply our natural language processing concepts on it. Cleaning is done in two main ways. Making sure everything is in lowercase and secondly we remove all the unwanted characters from it like punctuations. But even before that we need to read text in our python program. We need to convert it to lowercase because the words are the soul of analyzing text. And when we compare words in natural language processing a word like an Apple with a capital A, is not equal to the same word in small case, for example an apple with a small 'a'. Therefore to compare words we need to make sure the entire text which we are going to be analyzing is in lower case. This is will make more sense as we go further along the videos.
- Recap and in this we video we are going to learn about the Natural Language Processing Emotion Algorithm. - Emotions.txt NLP Emotion Algorithm 1 Check if the word in the final word list is also present in emotion.txt - open the emotion file - Loop through each line and clear it - Extract the word and emotion using split 2 If word is present . Add the emotion to emotion_list 3 Finally count each emotion in the emotion list
- Python Level: Beginner. I am going to assume that you already know the Python basics ( variables, functions etc. )
- Please watch the preview lectures and read the description of this course before enrolling.
Welcome to this course on Sentiment and Emotion/Mood analysis using Python
Have you ever thought about how Politicians use Sentiment Analysis? They use to find which topics to talk about in public. A topic can have different sentiments (positive or negative) and varying emotions associated with it. Politicians analyze tweets/internet content to find out these topics and use them to find holes in the opposition.
How Google Maps classifies millions of locations like Restaurants by analyzing the Reviews
How Amazon shows products which evoke Positive Sentiments/Emotions for the buyers
How KFC use it to do Market Research and Competitor Analysis
If you want to know Technology running behind, this is the Sentiment Analysis/Mood Analysis course which is going to use Natural Language Processing ( NLP ) and Text Mining to analyze different moods in a text ( example - Sadness, Excitement, Loneliness etc)
- Developers wanting to analyze text and extract meaning & information from it.
- Beginner Python Developers who are curious about Natural Language Processing ( NLP )
- Anyone interested in learning about Sentiment and Emotion/Mood Analysis