What is Sentiment Analysis and How Is It Done?
Sentiment analysis refers to the approach that examines the general attitude of communities about a topic. Sentiment analysis is calculated by measuring the data taken from the ideas and opinions of individuals with special software. In this way, it can be understood what people think and feel about a product, service, brand or event.
It analyzes text data using technologies such as sentiment analysis, natural language processing (NLP), and machine learning (ML). Text data can be obtained from a variety of sources such as social media posts, comments, survey responses, news or blog posts. Sentiment analysis classifies these data as positive, negative, or neutral and assigns them a score. Thus, it is possible to measure the general mood of the communities.
Sentiment analysis is an important tool for businesses. Thanks to sentiment analysis, businesses can achieve goals such as increasing customer satisfaction, improving product and service quality, optimizing marketing strategies and providing competitive advantage. Sentiment analysis can also be used in areas such as academic research, political campaigns, social movements or personal development.
There are many methods for performing sentiment analysis. Some of these are those:
- Word-based sentiment analysis: In this method, sentiment values of words in text data are taken from a dictionary and collected. In this way, the overall emotion score of the text is calculated.
- Machine learning-based sentiment analysis: In this method, text data is compared with pre-labeled training data and a model is created. This model is then used to classify new text data in terms of emotion.
- Deep learning-based sentiment analysis: In this method, text data is processed with complex artificial neural networks and sentiment scores are estimated. This method can give more precise and natural results.
Many tools and platforms are also available for performing sentiment analysis. Some of these are those:
- Google Cloud Natural Language: Google's cloud-based NLP service, this platform can analyze text data for sentiment and give a score.
- IBM Watson Natural Language Understanding: IBM's artificial intelligence service, this platform can analyze text data for emotion, tone, and emotional expressions and provide detailed reports.
- Microsoft Azure Text Analytics: Microsoft's cloud-based NLP service, this platform can classify text data in terms of emotion and assign a score.
Sentiment analysis is an increasingly popular and advanced technology today. With sentiment analysis, it is possible to take the pulse of communities and serve them better.