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 dataset for the sentiment analysis model is trained on these datasets, it knows how to behave when presented with new data in a similar vein. 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 dataset for 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 in video content analysis,lights 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 dataset for sentiment analysis datasets for various industries. 

dataset for sentiment analysis

Why Is Sentiment Analysis Important For Business?

Dataset for sentiment analysis is important to all marketing departments for brand insights. It is used for social media monitoring, brand reputation monitoring, the voice of the customer (VoC) data analysis, market research, patient experience analysis, and other functions. dataset for 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 dataset for 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. Sentiment analysis features.

Which are the top dataset for sentiment analysis datasets for machine learning?

Here are some top dataset for sentiment analysis datasets on various specialties and industries. They are free for download.

  1. Amazon product data.

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.

  1. OpinRank Review Dataset for hotels and cars.

This is one of those raredataset for 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.

  1. Yelp Dataset.

This dataset contains 5.2 million Yelp reviews with star ratings, businesses, reviews, and user data. It was part of the Yelp Dataset Challenge for students to conduct research or analysis on Yelp’s social media listening data. The dataset has information about businesses across 8 metropolitan areas in North America.

  1. Stanford Sentiment Dataset.

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.

  1. Cornell Movie Review Dataset.

This dataset for sentiment analysis dataset contains 2,000 positive and negatively tagged reviews. It also has more than 10,000 negative and positive tagged sentence texts.

  1. Lexicoder Sentiment Dictionary.

Another one of the key dataset for 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.

  1. Twitter US Airline Dataset.

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 dataset for 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.

  1. Opinion Lexicon.

This dataset provides a list of close to 7000 positive and negative opinion words or sentiment words in English.

  1. Paper Reviews Dataset.

One of the best dataset for 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.

  1. First GOP Debate Twitter Sentiment.

This dataset for 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.

  1. IMDB Reviews Dataset.

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.

  1. Sentiment Polarity Lexicons For Languages.

Among the many dataset for 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 sntiment analysis based on a knowledge graph.

Sentimental analysis has found its applications in various fields that are now helping enterprises to estimate and learn from their clients or customers correctly. Sentiment analysis is increasingly being used for social media monitoring, brand monitoring, the voice of the customer (VoC), customer service, and market research. Sentiment analysis uses NLP methods and algorithms that are either rule-based, hybrid, or rely on machine learning techniques to learn data from datasets.

The data needed in sentiment analysis should be specialised and are required in large quantities. The most challenging part about the sentiment analysis training process isn’t finding data in large amounts; instead, it is to find the relevant datasets. These data sets must cover a wide area of sentiment analysis applications and use cases.

  • Stanford Sentiment Treebank.

This dataset contains just over 10,000 pieces of Stanford data from HTML files of Rotten Tomatoes. The sentiments are rated between 1 and 25, where one is the most negative and 25 is the most positive. The deep learning model by Stanford has been built on the representation of sentences based on the sentence structure instead just giving points based on the positive and negative words. 

For example:

The Interview was neither that funny nor that witty. Even if there are words like funny and witty, the overall structure is a negative type.

  • Multi-Domain Sentiment Dataset.

This dataset contains positive and negative files for thousands of Amazon products. Although the reviews are for older products, this data set is excellent to use. The data derives from the Department of Computer Science at John Hopkins University.

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