Top Sources Of Sentiment Analysis Datasets
To train a sentiment analysis model, we need machine learning techniques to help the model learn data patterns from specialized sentiment analysis datasets. Powered by artificial intelligence, when the sentiment analysis model is trained on these datasets, it knows how to behave when presented with new data in a similar vein; improving the accuracy of data analysis stage of sentiment analysis process. If you are a company in the hospitality industry, you will need a model that has been trained on datasets that are collected and tagged from the hospitality industry. And so is the case with all industry verticals.
Such datasets need to be very wide in their scope of sentiment analysis applications and business cases. An efficiently trained sentiment model that can accurately analyze sentiment from text as well as videos, through video content analysis, is an invaluable asset for business intelligence. It can help you gain customer insights from not only reviews and surveys but also social platforms like YouTube, TikTok, Facebook, etc.
In the article, we present the top sources for great sentiment analysis datasets for various industries.
Why Is Sentiment Analysis Important For Business?
Sentiment analysis is important to all marketing departments for brand insights. It is used for social media monitoring, brand reputation monitoring, voice of the customer (VoC) data analysis, market research, patient experience analysis, and other functions. Sentiment analysis features employ the use of natural language processing (NLP) tasks and named entity recognition (NER) to identify and categorize entities and topics present in the data.
With an aspect-based sentiment analysis (ABSA) approach, companies can find extremely fine-grained insights from all sources of data for insights such as patient notes, EMRs, customer call logs, etc. There are however challenges that companies sometimes face while conducting sentiment analysis. You can read about sentiment analysis challenges and the solutions here.
Which are the top sentiment analysis datasets for machine learning?
Here are some top sentiment analysis datasets on various specialties and industries. They are free for download.
This dataset has amazon product reviews and metadata including 142.8 million reviews spanning May 1996 to July 2014. It has reviews including ratings, text, and helpfulness votes. Product metadata includes descriptions, brand, category, price, and image features. The dataset also has links to views and purchase graphs. Read more about sentiment analysis on large scale amazon product reviews.
This is one of those rare sentiment analysis datasets that has complete reviews on both the automotive and the hotel industries. It has 2,59,000 hotel reviews and 42,230 car reviews collected from TripAdvisor and Edmunds, respectively. Details include dates, favorite hotels and car models, user names, and the full review in text. The dataset contains information from 10 different cities including Dubai, Beijing, Las Vegas, and San Fransisco.
This dataset gives you recursive deep models for semantic compositionality over a sentiment treebank. It has more than 10,000 pieces of Stanford data from HTML files of Rotten Tomatoes.
This sentiment analysis dataset contains 2,000 positive and negatively tagged reviews. It also has more than 10,000 negative and positive tagged sentence texts.
Another one of the key sentiment analysis datasets, this one is meant to be used within the Lexicoder that performs the content analysis. The dictionary has 2,800+ negative sentiment words and 1,709 positive sentiment words.
This dataset contains tweets about all the major US airlines, since Feb 2015. It includes the Twitter user IDs, sentiment confidence score, negative and positive reasons, retweet counts, tweet text, date, time, and location.
This sentiment analysis dataset comprises positive and negative tagged reviews for thousands of Amazon products. The reviews contain ratings from 1 to 5 stars, which can be converted to binary if required.
This dataset provides a list of close to 7000 positive and negative opinion words or sentiment words in English.
One of the best sentiment analysis datasets in the English and Spanish languages, it gives reviews on computing and informatics conferences. You will notice a difference between how the paper is evaluated versus how the review was written by the original reviewer.
This sentiment analysis dataset consists of around 14,000 labeled tweets that are positive, neutral, and negative about the first GOP debate that happened in 2016.
This dataset contains 50K movie reviews from IMDB that can be used for binary sentiment classification. There are a set of 25,000 highly polar movie reviews for training and 25,000 for testing.
Among the many sentiment analysis datasets in multiple languages, this one is the most generous. It contains positive and negative sentiment lexicons for 81 languages. The sentiments were built based on English sentiment lexicons. The lexica were generated through graph propagation for the sentiment analysis based on a knowledge graph.
Click here to understand major sentiment analysis applications.
Finding The Right Sentiment Analysis API
Repustate’s sentiment analysis platform has been trained on sentiment analysis datasets in multiple industries. The engine processes millions of reviews per day for hundreds of clients across the globe. It enables real-time social media sentiment analysis and does so in 23 languages, natively. It provides topic-driven and aspect-based sentiment analysis and has a processing speed is 1,000 reviews per second.
Highly customizable and scalable, Repustate’s sentiment analysis API has been instrumental in supporting companies across industries in their business endeavors. From helping AARP develop a brand new diet program, to providing vital information to the Kingdom of Saudia Arabia in its healthcare plan, our solution helps you keep score of about each aspect of your business.
Additionally, our sentiment visualization dashboard gives you insights in graphs and charts so you can understand your data easily and get actionable insights