![]() ![]() 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. 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. ![]() 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 is important to all marketing departments for brand insights. Why Is Sentiment Analysis Important For Business? In the article, we present the top sources for great sentiment analysis datasets for various industries. It can help you gain customer insights from not only reviews and surveys but also social platforms like YouTube, TikTok, Facebook, etc. 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. Such datasets need to be very wide in their scope of sentiment analysis applications and business cases. And so is the case with all industry verticals. 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. 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. ![]() To train a sentiment analysis model, we need machine learning techniques to help the model learn data patterns from specialized sentiment analysis datasets. Top Sources Of Sentiment Analysis Datasets ![]()
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