10 Set Analysis Data Sets – Analytics Vidhyya

Introduction

Fire analysis is a powerful technique used to determine the emotional tone behind a series of texts, such as social media publications, customer criticism or reports. By analyzing the feeling expressed in these texts, companies and organizations can obtain valuable information about public opinion, customer satisfaction and brand perception. In this article, we will explore the top 10 feelings of feelings analysis data that can be used to train machine learning models and improve the precision of feelings analysis algorithms.

Understand the analysis of feelings and its importance

The analysis of feelings, also known as opinion mining, is the process of extracting subjective information from the text and categorizing it as positive, negative or neutral. It involves Natural Language Processing Techniques (PNL) to analyze the feeling expressed in a given text and provide a quantitative measure of the polarity of the feeling.

The importance of feelings analysis cannot be exaggerated. It allows companies to understand customer feedback, monitor brand reputation, and make data -based decisions. By analyzing the feeling, companies can identify areas to improve, detect emerging trends and adapt their marketing strategies to better meet customer needs.

Benefits of using feelings analysis data sets

Using high quality feelings analysis data sets It is crucial to training precise machine learning models. These data sets provide various texts with labeled sentiment, allowing algorithms to learn patterns and make precise predictions. When using such data sets, companies can improve the performance of their feelings analysis systems and get more reliable information.

Overview of feelings analysis data sets

In this section, we will explore the 10 best sets of feelings analysis data widely used by field researchers. These data sets cover various domains, including social networks, product reviews and reports, ensuring a complete understanding of feelings analysis in different contexts.

Dataset 1: Sentment on Social Networks

Link to the data set: Feeling of social media

Data Set Description: This data set is made up of social media posts on various platforms. Includes tags of positive and negative feelings, which allow to form models of analysis of feelings on real social media data in the real world.

Data Data 2: Amazon’s criticisms

Link to the data set: Amazon comments

Data Set Description: This data set focuses on customer reviews of a popular e -commerce platform. It contains a large number of reviews with corresponding feeling labels, allowing the development of feelings analysis models.

DATA DATE 3: All news

Link to the data set: All the news

Data Set Description: This data set includes reports of respectable sources on different topics, such as politics, sport and entertainment. It offers feelings tags for each article, allowing the analysis of feeling in the media.

Dataset 4: Cornell Movie Review Dataset

Link to the data set: Cornell Movie Dataset

Data Set Description: This data set contains films criticism from a well -known movie review website. Includes feelings tags for each review, making it an ideal choice for the formation of feelings analysis models in movies criticism.

5 Data Set: Airline Twitter Sentiment

Link to the data set: Airline Twitter Sentment

Data Set Description: This data set focuses on customer feedback for a leading airplane company. Includes feelings tags for each feedback, allowing you to analyze the feeling of customers in the airline industry.

Link to the data set: Disasters on social media

Data Set Description: Collaborators have met more than 10,000 tweets gathered through various searches such as “ablice”, “quarantine” and “Pandemonio”. Each tweet was scored based on whether it referred to a disaster event, distinguish it from jokes, films’ criticisms or non -disaster content.

Dataset 7: Products and Emotions

Link to the data set: Products and emotions

Data Set Description: This data set includes product reviews from a popular online market. Includes feelings tags for each review, which makes it a valuable resource for training model analysis models in online purchases domain.

DATSET 8: Drug Magazine

Link to the data set: Drug review

Data Set Description: This data set focuses on the analysis of feelings in the health domain. It contains patient reviews on specific medicines and related conditions and a 10 -star patient classification that reflects the overall satisfaction of the patient.

Dataset 9: Apple’s feeling

Link to the data set: Apple feeling

Data Set Description: This data set is composed of publications on social media related to a specific brand or product. Includes feelings tags for each post, allowing brand feelings and reputation management analysis.

Data data 10: Hotel criticisms

Link to the data set: Hotel reviews

Data Set Description: This data set includes customer reviews on a leading hotel chain. It offers feelings tags for each review, allowing customer’s feelings analysis in the hospitality industry.

Conclusion

In conclusion, feelings analysis data sets are crucial to training precise machine learning models for feelings analysis. When using the 10 best data sets mentioned in this article, companies and organizations can improve their understanding of customer feeling, improve brand reputation and make data -based decisions. These data sets cover various domains and provide valuable information on feelings analysis in various contexts. When taking advantage of these data sets, companies can get a competitive advantage in the world driven by today’s data. However, you can raise the domain of data science with our AI/ML BLACKBEL PLUS PROGRAMDesigned to offer a complete learning experience that allows you.