Abstract
Sentimental analysis and opinion extraction are emerging fields at AI. These approaches help organizations to use the opinions, sentiments, and subjectivity of their consumers in decision-making. Sentiments, views, and opinions show the feeling of the consumers towards a given product or service. In recent years, Opinion Mining and Sentiment Analysis has become an important tool to detect the factors affecting mental health. It’s Also true that human biasness is available in giving opinions, but it can be eliminated through the use of algorithms to get better results. However, it is crucial to remember that the developers are human and might pass the biasness to the algorithms during training. The main target of this paper is to give background knowledge on opinion extraction and sentimental analysis and how factors affecting mental health can be collected. The paper aimed to use interested individuals in knowing some of the algorithms in opinions extraction and sentimental analysis. The paper also provides benefits of using sentiment analysis and some of the challenges of using the algorithms.
Highlights
Sentiment analysis refers to the process of detecting positive or negative sentiment in a text
Sentiment analysis has become an essential tool needed by every organization to help in monitoring and understanding the opinions of their customers
This approach is based on an algorithm that clearly defines the description of opinion to the entity
Summary
Sentiment analysis refers to the process of detecting positive or negative sentiment in a text. Most organizations have used sentiment analysis to improve their products and service provision. Sentiment analysis has become an essential tool needed by every organization to help in monitoring and understanding the opinions of their customers. Organizations can use opinions in survey responses and social media conversations to understand their customer feedback and tailor products to meet their needs. The best products are rated under very positive, and the poorest are rated very negative Emotion detection is another type of sentiment analysis centered on emotions such as happiness, frustrations, anger, and sadness. Customers use text sentiments in product reviews of various products In some instances, they might concentrate on the multiple aspects of development such as battery life or camera quality for mobile phones. Aspect-based sentiment analysis can allow organizations to understand the aspects that were rated positive, negative, or neutral. Idioms and metaphors are used in implicit opinions, which makes sentiment analysis complex
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