Abstract
Sentiment analysis is an emerging application of NLP (Natural Language Processing). This is also called opinion mining or attitude detection. In the text mining field, Sentiment Analysis is continuous area of research. It is a procedural treatment of attitude, feelings and textual content. The fundamental thought is to discover text polarity and order it as positive, negative, or neutral. It supports human to take a good judgment. This survey paper gives an extensive summary of the previous updates in this area. Several currently proposed algorithms and numerous upgrades to different SA applications have been investigated and summed up in this review. These articles are classified by their commitment to different SA techniques. Areas identified with SA (business monitoring, polarity observation, social media monitoring) that has recently attracted researchers is discussed. The fundamental objective of this survey is to provide a complete picture of SA practices and associated fields with brief explanation. The significant role of this study includes a refined classification of current papers and a depiction of ongoing patterns in sentiment analysis and research in its associated fields.
Highlights
Sentiment analysis is known as Opinion mining
It tends to be highly valuable in trading field, social media fields, and so forth
The mostly technique used in theoretical type is POS tagging and lexicon-based methods but practically there are a lot of ML technique and some deep learning technique like LSTM, GRNN used to classify SA discussed in table 1
Summary
Sentiment analysis is known as Opinion mining. It tends to be highly valuable in trading field, social media fields, and so forth. Before purchasing any product from e-commerce sites, the customer first checks the validity of a positive, negative or neutral product They likewise utilize these websites to realize what other client's assessment about the item are. C) Neutral: I usually remain hungry until early afternoon (this sentence is subjective because it contains user thoughts and feelings but is neutral because it has no positive or negative polarity.). Iii) Sentiment Analysis- It can be carried out at various level: a) Sentence Level Analysis: At this stage, the task is to decide whether each sentence expresses feelings This stage indicates target sentences that express real data and subjective opinions. C) Aspect Level Analysis: At this stage, we can do better investigation and this stage requires the usage of natural language processing At this stage, the description of feelings can be done through polarity and the objective of opinion. The remedies are two-fold: in question we first recognize the elements and aspects of the unit, and afterwards on each aspect access the response
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