Twitter Sentiment Analysis Java Github







We can also use third party library to find the sentiment analysis. The Azure Text Analytics API also provides a Linux-based Docker container image for sentiment analysis, so you can install and run the Text Analytics container close to your data. Basic Sentiment Analysis with Python. How to build your own Facebook Sentiment Analysis Tool. The sentiment of a tweet is equivalent to the sum of the sentiment scores for each term in the clean tweet. Like it has been previously said, a language is just a means to achieve your goal. On the Create dataset page, enter a name for the dataset and select Sentiment analysis as the objective. …My aim is to see these stats. Recent efforts among the R text analysis developers’ community are designed to promote this interoperability to maximize flexibility and choice among users. edu Arpit Goel Stanford University [email protected] Sentiment Analysis can help you. edu ABSTRACT Twitter is a micro-blogging website that allows people to share and express their views about topics, or post messages. Report and track bugs or request new features by creating an issue on GitHub. You can find Jupyter Notebook file from below Github repo. SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter INTRODUCTION One of the most difficult problems when assigning either positive or negative polarity in sentiment analysis tasks is to accurately determine what is the truth value of a certain statement. Love Scala, Java, and C#. Next, we load the model related to sentiment analysis en-sentiment. A sentiment analysis system for text analysis combines natural language processing. Extracting and Mining Twitter Data Using Zapier, RapidMiner and Google/Microsoft Tools. Twitter Sentiment template¶ The feed template for this tutorial is provided in Kylo github. Amazon Food Reviews - Analysing ~500,000 food reviews from Amazon fine food reviews. The goal of this project is to learn how to pull twitter data, using the tweepy wrapper around the twitter API, and how to perform simple sentiment analysis using the vaderSentiment library. Email Sentiment Analysis with Stanford NLP. 16 minute read. let's say that we want to know the sentiment of tweets about BigData and Software Engineer @MapRTechnologies. This piece is based on the video Twitter Sentiment Analysis — Learn Python for Data Science #2 by Siraj Raval. Christopher Healey, Goodnight Distinguished Professor in the Institute of Advanced Analytics at North Carolina State University, has built one of the most robust and highly functional free tools for Twitter sentiment analysis out there: the Tweet Visualizer. The sentiments are part of the AFINN-111. Why sentiment analysis?. One of the tasks in aspect-based sentiment analysis is to extract aspect and opinion terms from review text. We can also use third party library to find the sentiment analysis. npm i twitter sentiment --save. Because of the many online resources that exist that describe what Naïve Bayes is, in this post I plan on demonstrating one method of implementing it to create a: Binary sentiment analysis of. A sentiment analysis system for text analysis combines natural language processing. A classic argument for why using a bag of words model doesn't work properly for sentiment analysis. Twitter is now a hugely valuable resource from which you can extract insights by using text mining tools like sentiment analysis. The Neik Sanders Twitter Sentiment Analysis corpus. Sentiment Analysis Tools Overview, Part 1. They classify Tweets for a query term into negative or positive sentiment. Watch Queue Queue. UPDATE: The github repo for twitter sentiment analyzer now contains updated get_twitter_data. This sentiment mean is continuously sent to connected browser clients and displayed in a sparkline graph. Twitter Sentiment Analysis offers organizations a fast and effective way to analyze customers perspective toward the critical to success in the market place. First of all, we need to have Python installed. let's say that we want to know the sentiment of tweets about BigData and Software Engineer @MapRTechnologies. Sentiment Analysis of Twitter using Spark It is done using a JAVA library called Language Tool. storm java twitter-sentiment-analysis Twitter sentiment. Our study focuses on evaluating transfer learning using BERT (Devlin et al. However, it's worth mentioning that don't exist perfect tools in this field. Cancel anytime. Sentiment analysis, which is also called opinion mining, uses social media analytics tools to determine attitudes toward a product or idea. Personality insights from tweets Psychologists have created a site where you can plug in your Twitter handle, and get a scientifically grounded analysis of your. The ability to install third-party Java packages into a Python Notebook. Sentiment analysis of the headlines are going to be performed and then the output of the sentiment analysis is going to be fed into machine learning models to predict the price of DJIA stock indices. We can also use third party library to find the sentiment analysis. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. Sentiment Analysis API. Sentiment analysis is a method of analyzing a piece of text and deciding whether the writing is positive, negative or neutral. This integration lets you use the computing power of Functions in orchestrations with other Azure and third-party services. Here is an example of analyzing entity sentiment provided as a string:. Sentiment Analysis is the measurement of positive and negative language. I will show you how to create a simple application in R and Shiny to perform Twitter Sentiment Analysis in real-time. A classic machine learning approach would. It can be tested by placing appropriate oauth credentials in config. Performance Timeline Key Word Cloud. While searching around these NLP frameworks, I used the following simple test. In this short series (two parts - second part can be found HERE) I want to expand on the subject of sentiment analysis of Twitter data through data mining techniques. Hey there! After my post about sentiment analysis using the Viralheat API I found another service. Analyzing Stock Market Movements Using Twitter Sentiment Analysis system using Guba sentiment analysis, Personal and Ubiquitous Computing, v. Today’s algorithm-based sentiment analysis tools can handle huge volumes of customer. The first model I tried was the CNN-LSTM Model. The goal of this project is to learn how to pull twitter data, using the tweepy wrapper around the twitter API, and how to perform simple sentiment analysis using the vaderSentiment library. Why work with us?. Sentiment analysis is the process of analyzing the opinions of a person, a thing or a topic expressed in a piece of text. java This section shows a few snippets to demonstrate the process that was carried out for pre-processing Twitter streaming data for sentiment analysis. Consumers can use sentiment analysis to research products or services before making a purchase. 07/09/2019; 13 minutes to read +13; In this article. This is the fifth article in the series of articles on NLP for Python. GitHub Gist: instantly share code, notes, and snippets. Sentiment Analysis Java Project Github RStudio Desktop and the Github repo Discuss the strengths and caveats of these projects and how best to outline. Sentiment Analysis¶ Now, we'll use sentiment analysis to describe what proportion of lyrics of these artists are positive, negative or neutral. For those interested in coding Twitter Sentiment Analyis from scratch, there is a Coursera course " Data Science " with python code on GitHub (as part of assignment 1 - link). A classic argument for why using a bag of words model doesn't work properly for sentiment analysis. Sentiment determines whether a tweeter feels negatively or positively about a topic by comparing the words in a tweet to a lexicon of words that have positive valences or negative ones. Posts about Sentiment Analysis written by milindjagre. You will need to set up a couple of things before we can get started with coding. Realtime stream processing using Apache Storm – Part 1. On the Create dataset page, enter a name for the dataset and select Sentiment analysis as the objective. ThunderGod here with some Thunder Code! Presenting the Newspaper Sentiment analysis-inator! This little script downloads and analyzes newspaper articles to find if. Sentiment Analysis on Twitter. They classify Tweets for a query term into negative or positive sentiment. Sentiment Analysis of Twitter data can help companies obtain qualitative insights to understand how people are talking about their brand. They basically represent the same field of study. Entity-level Sentiment Analysis. The sentiment of a tweet is equivalent to the sum of the sentiment scores for each term in the clean tweet. it will be about Twitter sentiment analysis. One of the tasks in aspect-based sentiment analysis is to extract aspect and opinion terms from review text. Having previously wired up a simple spring app with Twitter to consume their tweet stream relating to last year's Rugby World Cup - mostly just to experiment with the event-driven programming model in Spring and Reactor - I thought on a whim, why not see if I can find some nice sentiment analysis tools to analyse the tweets, so rather than just consuming the number of tweets about a given. Flexible Data Ingestion. github; Projects. The first post is here. They can choose to "retweet" or share a tweet, to promote ideas that they find favorable and elect to follow others whose opinion that they value. Cancel anytime. Deeply Moving: Deep Learning for Sentiment Analysis. In sentiment analysis predefined sentiment labels, such as "positive" or "negative" are assigned to texts. Real-time Twitter Sentiment Analytics with TensorFlow and Spring Cloud Dataflow Performs a sentiment analysis of an real-time twitter stream. VADER Sentiment Analysis. View on GitHub Twitter Sentiment Analysis. Sentiment Analysis API. For this post I did one classifier with a deep learning approach. [6] Hassan Saif, Yulan He, and Harith Alani. It was essentially a function that maps a word to a pre-defined sentiment type (positive or negative) or a value (how positive or how negative). it will be about Twitter sentiment analysis. Sentiment analysis will derive whether the person has a positive opinion or negative opinion or neutral opinion about that topic. Read the analytics. Real-time Twitter sentiment analysis in Azure Stream Analytics. Basic data analysis on Twitter with Python. Screenshots of algorithm evaluation, analysis in one minute, results from scala server and spark streaming instrumentation. Using this data, we’ll build a sentiment analysis model with nltk. System design. This project is to create a “Sentiment Analysis” on a particular word or phrase from twitter. We also discussed text mining and sentiment analysis using python. Sentiment Analysis. Interestingly, I enrolled for a course on Sentiment analysis on Quantra, but my focus is more towards t. DeepQL - A Language for Querying a Deep neural Network Ampere - A Framework for High-Performance Battery Models WYNS - An Interactive Map of Twitter Sentiment Analysis DeepChess - A Reimplementation in Keras. There are some limitations to this research. Since the Stanford NLP library is written in Java, we will want to build the analysis engine in Java. We are using NY Times Archive API to gather the news website articles data over the span of 10 years. edu ABSTRACT In this paper, we apply sentiment analysis and machine learning principles to find the correlation between "public sentiment"and "market sentiment". We will […]. You are provided with a skeleton file tweet_sentiment. They can choose to "retweet" or share a tweet, to promote ideas that they find favorable and elect to follow others whose opinion that they value. 1 Pre-processing of the datasets A tweet contains a lot of opinions about the data which are expressed in different ways by different users. AFINN is a list of words rated for valence with an integer between minus five (negative) and plus five (positive). Look at the sentiment score of each tweet and the network of interactions among Twitter accounts. You can find working solutions, for example here. The most popular positive and negative words databases that can help to perform sentiment analysis were described: Liu and Hu opinion lexicon. It’s looking beyond the number of Likes, Shares or Comments you get on an ad campaign, product release, blog post, and video to understand how people are responding. Twitter is a popular micro-blogging service where users create status messages (called "tweets"). Hey there! After my post about sentiment analysis using the Viralheat API I found another service. into the Kylo templates UI. …Here I can see a summary for the last 28 days,…and a comparison with the 28 days before that. So, here we will join the dictionary dataset containing the. In our previous post, we had discussed how to perform Sentiment Analysis on the tweets using Pig. AIM OF THE PROJECT The purpose of this project is to build an algorithm that can accurately classify Twitter messages as positive or negative, with respect to a query term. On Tuesday, we walked through how to build a cluster with our Sentiment Analysis Sample application and how to get the app running. People seem to like this which is great,…and I'll know to post similar kinds of content again. WEKA; AngularJS; Bootstrap; jQuery; twitter4j (Java library for the Twitter API) General Concept. In this blog, we will perform twitter sentiment analysis using Spark. Java Microservices I tried to do sentiment analysis on the Twitter airline dataset. You are provided with a skeleton file tweet_sentiment. Project Lucy plus Twitter Sentiment Analysis Description. Most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. classify import NaiveBayesClassifier >>> from nltk. Introduction. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. Extract twitter data using tweepy and learn how to handle it using pandas. We will do so by following a sequence of steps needed to solve a general sentiment analysis problem. Thanks for this @yanofsky - its awesome code. Posts about Sentiment Analysis written by milindjagre. This tutorial shows you how to use Functions with Logic Apps and Cognitive Services on Azure to run sentiment analysis from Twitter posts. Semantic sentiment analysis of twitter. You consume the…. I used the twitter streaming API to pull 5000 tweets that were tagged with #Applewatch. The task, defined in a simplistic way, consists of determining the polarity of a text utterance according to the opinion or sentiment of the speaker or writer, as positive or negative. classify import NaiveBayesClassifier >>> from nltk. Use Twitter API and vaderSentiment to perform sentiment analysis. Because of the many online resources that exist that describe what Naïve Bayes is, in this post I plan on demonstrating one method of implementing it to create a: Binary sentiment analysis of. Use Case - Twitter Sentiment Analysis. Now that I know a bit of coding and there are libraries lying around on GitHub, I planned to give it another shot. In this blog, I will walk you through how to conduct a step-by-step sentiment analysis using United Airlines' Tweets as an example. Email Sentiment Analysis with Stanford NLP. The Twitter data used for this particular experiment was a mix of two datasets: The University of Michigan Kaggle competition dataset. This sentiment mean is continuously sent to connected browser clients and displayed in a sparkline graph. One of the tasks in aspect-based sentiment analysis is to extract aspect and opinion terms from review text. Semantic sentiment analysis of twitter. The most popular positive and negative words databases that can help to perform sentiment analysis were described: Liu and Hu opinion lexicon. In this project, you will analyze Twitter data to determine how people within a geographical region feel about a topic compared to people outside that region. Word embeddings that are produced by word2vec are generally used to learn context produce highand -dimensional vectors in a space. In 2013, there was a rumor on Twitter saying the White House being bombed, The computers responded instantly and automatically by performing trading, causing the stock market to fall immediately. In this blog, I will walk you through how to conduct a step-by-step sentiment analysis using United Airlines' Tweets as an example. However, this alone does not make it an easy task (in terms of programming time, not in accuracy as larger piece. Since the Stanford NLP library is written in Java, we will want to build the analysis engine in Java. Baseline Sentiment Analysis with WEKA Sentiment Analysis (and/or Opinion Mining) is one of the hottest topics in Natural Language Processing nowadays. [6] Hassan Saif, Yulan He, and Harith Alani. The twitter. More than 40 million people use GitHub to discover, fork, and contribute to over 100 million projects. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. As you can see, references to the United Airlines brand grew exponentially since April 10 th and the emotions of the tweets greatly skewed towards negative. The first thing. Tags: Machine Learning Live Feed Streaming Windowing Aggregation IMDG Storage Custom Source Twitter Pipeline API Tweet sentiment is analyzed in real-time to compute cryptocurrency popularity trends. Cancel anytime. We will download twitter feeds on a subject and compare it to a database of positive, negative words. Recently I designed a relatively simple code in R to analyze the content of Twitter posts by using the categories identified as positive, negative and neutral. The program was first used to pull and analyze Tweets, so I could get a better sense of how to clean the tweets so TextBlob can perform accurate. 🏆 SOTA for Sentiment Analysis on IMDb(Accuracy metric) you can also follow us on Twitter. Today’s algorithm-based sentiment analysis tools can handle huge volumes of customer. Skip to content. >> from nltk. , the MPQA corpus (Wiebe et al. Click a running history, and navigate to Get logs of a container action. In our previous post, we had discussed how to perform Sentiment Analysis on the tweets using Pig. Sentiment Analysis of Twitter Data | Final Year Projects 2016 Twitter Sentiment Analysis in Python using Tweepy and TextBlob Sentiment Analysis and Wordcloud with R from Twitter. In this article, I will demonstrate how to do sentiment analysis using Twitter data using. Step by step Tutorial on Twitter Sentiment Analysis and n-gram with Hadoop and Hive SQL - TwitterSentimentAnalysisAndN-gramWithHadoopAndHiveSQL. Sets the query; Extract tweet; Sets the tweet text; Set the amount of tweet; Sentiment Analysis. zip Download. g - What people think about Trump winning the next election or Usain Bolt finishing the race in 7. The Neik Sanders Twitter Sentiment Analysis corpus. Performance Timeline Key Word Cloud. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. it is not loa. How to setup and use Stanford CoreNLP Server with Python; Japanese. However, stable Twitter sentiment classification performance remains elusive due to several issues: heavy class imbalance in a multi-class problem, representational richness issues for sentiment cues, and the use of diverse colloquial linguistic patterns. One of the most important things that can be a signal of a successful product is the users want to use it since it fulfills their needs. I have written blog posts on using spark streaming to analyze twitter data and also integrate spark with kafka and flume. You can learn more about PixieDust on GitHub 0. View on GitHub Twitter Sentiment Analysis. GitHub Gist: instantly share code, notes, and snippets. That should change going forward. It could be. This template will allow you to create a feed to monitor tweets based on keywords and write the sentiment results to a Hive table. The source of the analysis is a collection of tweets. generating a huge amount of sentiment rich data in the form of tweets, status updates, reviews and blog posts etc. 09/30/2019; 12 minutes to read +15; In this article. Public opinion views about government policies are scattered across the Internet, in Twitter and News Feeds. Usually, it refers to extracting sentiment from text, e. I recommend using 1/10 of the corpus for testing your algorithm, while the rest can be dedicated towards training whatever algorithm you are using to classify sentiment. In my previous article, I explained how Python's spaCy library can be used to perform parts of speech tagging and named entity recognition. Twitter sentiment analysis is arduous as. View on GitHub Download. We’ve closed the doors on new submissions and now the voting for the most beloved plugin begins!. We will do so by following a sequence of steps needed to solve a general sentiment analysis problem. While in industry, the term sentiment analysis is more commonly used, but in academia both sentiment analysis and opinion mining are frequently employed. First of all, we need to have Python installed. GitHub Gist: instantly share code, notes, and snippets. binary classification for Twitter sentiment analysis. Data guarantees in Spark Streaming with kafka integration. This post - Sentiment Analysis - Find out what customers think of your brand or topic by analyzing raw text for clues about positive or negative sentiment. […] The post Twitter sentiment analysis with Machine Learning in R using doc2vec approach appeared first on AnalyzeCore - data is beautiful, data is a story. Twitter sentiment demo from my I/O talk. Sign in Sign up. This is considered sentiment analysis and this tutorial will walk you through a simple approach to perform sentiment analysis. Check out this list of 20+ Sentiment Analysis API for consumption on Mashape, available in multiple language, both free and paid. Another Twitter sentiment analysis with Python — Part 1. It is a rapidly. You may also like: Spark Streaming part 1: Real time twitter sentiment analysis. I just need to know whether the article is generally positive, ne. I have developed an application which gives you sentiments in the tweets for a given set of keywords. This article is a tutorial on creating a sentiment analysis application that runs on Node. Project Lucy is basically a collection of programs which interact with the Twitter API. Basic data analysis on Twitter with Python. Sentiment analysis of this user generated data is very useful in knowing the opinion of the crowd. Open the AutoML Natural Language Sentiment Analysis UI, select the Launch app link in the AutoML Sentiment Analysis box, and click the lightbulb icon in the left navigation bar to display the available models. We will be doing stream processing using Spark Structured Streaming, and sentiment analysis on text data with Cognitive Services APIs as an example. To get acquainted with the crisis of Chennai Floods, 2015 you can read the complete study. Step 2 - Create Github App. This allows an algorithm to compose sophisticated functionality using other algorithms as building blocks, however it also carries the potential of incurring additional royalty and usage costs from any algorithm that it calls. Skip to content. Used twitter API. Employ sentiment analysis to identify patterns in positive or negative content over time; Assess discrepancies between the sentiment value of Twitter content through identified communication pathways and events that occur on the ground. Sentiment Analysis, is receiving a big attention these days, because of its huge spectrum of applications ranging from product review analysis, campaign feedback, competition bench-marking, customer profiles, political trends, etc There is a huge flow of information going through the internet and social networks. Extract twitter data using tweepy and learn how to handle it using pandas. The goal of this project is to learn how to pull twitter data, using the tweepy wrapper around the twitter API, and how to perform simple sentiment analysis using the vaderSentiment library. Twitter is now a hugely valuable resource from which you can extract insights by using text mining tools like sentiment analysis. , the MPQA corpus (Wiebe et al. Recent efforts among the R text analysis developers’ community are designed to promote this interoperability to maximize flexibility and choice among users. The combination of these two tools resulted in a 79% classification model accuracy. "I like the product" and "I do not like the product" should be opposites. Skip to content. Published: December 26, 2016 Introduction. Once your app is created, you need to copy the Client Key and the Client Secret from the GitHub app to Firebase. A few days ago, I also wrote about how you can do sentiment analysis in Python using TextBlob API. SmartPOS /Point of Sale Web with ERP SmartPOS 5. I have developed an application which gives you sentiments in the tweets for a given set of keywords. After reviewing the Text Analysis API, I noticed three capabilities that I wanted to take a further look at:. spark-streaming twitter-sentiment-analysis twitter. Lets start! Brief Discussion on Sentiment Analysis. You can find the previous posts from the below links. In this article, we talked about how to scrape tweets on Twitter using Octoparse. I also believe that Ramu is very focused in his profession and dedicated towards career in analytics. You will need to set up a couple of things before we can get started with coding. dataaspirant. Today for my 30 day challenge, I decided to learn how to use the Stanford CoreNLP Java API to perform sentiment analysis. We will start with preprocessing and cleaning of the raw text of the tweets. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Environment Setup. Performance Timeline Key Word Cloud. How to create a Twitter Sentiment Analysis using R and Shiny Sentiment Analysis can help you. dataset for Twitter sentiment analysis that targets sentiment annotation at both, tweet and entity levels. Real-time Twitter trend analysis is a great example of an analytics tool because the hashtag subscription model enables you to listen to specific keywords (hashtags) and develop sentiment analysis of the feed. Look at the sentiment score of each tweet and the network of interactions among Twitter accounts. Analyzing Entity Sentiment. Sentiment analysis. Performance Timeline Key Word Cloud. However, some hackers take advantage on the sentiment analysis on Twitter. model and the SCDF Twitter Sentiment Processor. Note: Since this file contains sensitive information do not add it. Otherwise:. It will be able to search twitter for a list of tweets about any topic we want, then analyze each. Now you can see the sentiment of your tweet. *If graphs don't appear, scroll up or down. Java Microservices I tried to do sentiment analysis on the Twitter airline dataset. Most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. [5] Efthymios Kouloumpis, Theresa Wilson, and Johanna Moore. You can find the GitHub project here. edu ABSTRACT Twitter is a micro-blogging website that allows people to share and express their views about topics, or post messages. See the complete profile on LinkedIn and discover Marvin M. View on GitHub Download. Download and import the Twitter Sentiment template. Visit our GitHub page to download our Java SDK repo, or you can run an example with the code below: Installation: Since the Text Analysis API is published to Maven Central, it is enough to add the dependency to the POM: Using Maven:. The ratio of the matched positive and negative words is the sentiment ratio. Now you can see the flow has running history. Why work with us?. Spark-MLlib-Twitter-Sentiment-Analysis - Analyze and visualize Twitter Sentiment on a world map using Spark MLlib OpenJDK 64-Bit v1. found on my GitHub e-mail, twitter. Sentiment Analysis and Topic Modeling on Twitter Data. Source: colah. GitHub Gist: instantly share code, notes, and snippets. I'm almost sure that all the. Checkout the project in my github repo. Much of it revolves around reviews for movies and products. Naive Bayes is an algorithm to perform sentiment analysis. Cancel anytime. But this API doesn´t just offer sentiment analysis, it offers a much more detailed analysis. They classify Tweets for a query term into negative or positive sentiment. Oct 9, 2016. This post - Sentiment Analysis - Find out what customers think of your brand or topic by analyzing raw text for clues about positive or negative sentiment. Posts about Sentiment Analysis written by milindjagre. You can find Jupyter Notebook file from below Github repo. …So this is the homepage of Twitter Analytics. This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Concepts Text Analytics uses a machine learning classification algorithm to generate a sentiment score between 0 and 1. binary classification for Twitter sentiment analysis. Recent tweets that contain your keyword are pulled from Twitter and visualized in the Sentiment tab as circles. Note: I'm using Azure, but the code doesn't depend on it. I opened two issues on Github because it seems that the TJBot project has not been maintained for a longer time period. If you want to know more about how the code works, see that tutorial. There are some limitations to this research. g - What people think about Trump winning the next election or Usain Bolt finishing the race in 7. Sentiment analysis is a technique used in Social media analytics to evaluate whether posts on a specific issue/Person are positive or negative.