Which is the best dataset for sentiment analysis?
The WordStat Sentiment Dictionary dataset for sentiment analysis was designed by integrating positive and negative words from the Harvard IV dictionary, the Regressive Imagery Dictionary, and the Linguistic and Word Count dictionary. It contains about 15,000 words of data combined.
Are there any dictionaries that do sentiment analysis?
It’s described in more detail in this paper and released under the GPL. Arriving a bit late I’ll just note that dictionaries have a limited contribution for sentiment analysis. Some sentiment bearing sentences do not contain any “sentiment” word – e.g. “read the book” which could be positive in a book review while negative in a movie review.
How to do a sentiment analysis in Python?
This example demonstrates how to assess sentiment computationally from a large corpus of economic news articles.
How many languages are there for sentiment data?
Sentiment Lexicons for 81 Languages contains languages from Afrikaans to Yiddish. This data includes both positive and negative sentiment lexicons for a total of 81 languages.
The algorithm used will predict the opinions of academic paper reviews. Most of the dataset for the sentiment analysis of this type is sent in Spanish. It has a total of instances of N=405 evaluated with a 5-point scale, -2: very negative, -1: neutral, 1: positive, 2: very positive.
How does sentiment analysis for Spanish sentences work?
How does it work? sentiment-spanish is a python library that uses convolutional neural networks to predict the sentiment of spanish sentences. The model was trained using over 800000 reviews of users of the pages eltenedor, decathlon, tripadvisor, filmaffinity and ebay .
How big is the Twitter sentiment dataset?
The Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment.