PDF Sentiment Analysis in Natural Language Processing International Research Group IJET JOURNAL

It is also another example of where sentiment analysis can help you to improve resource allocation and efficiency. Have you tried translating something recently and wondered how the program is understanding your original? Well, if it that will be relying on Natural Language Processing (NLP) with sentiment analysis to help identify the contextual meaning and nuance of what you are trying to translate. Finally, there is the topic model method which is an unsupervised specific algorithmic process which generates word clusters, referred to as “topics”. Each individual word may belong to more than one of these topics and further refinement and analysis of the data creates a sort of map which provides an insightful overview. Within the space of comparative opinions, just as within the space of sentiment analysis in general, there are statements that can be processed algorithmically to yield relevant and useful data, and ones that can’t.

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Types of Sentiment Analysis

A model developed on the basis of machine learning will be able to create patterns from the information you give it and predict the mood of the text. For example, say you’re a property management firm and want to create a repair ticket system for tenants based on a narrative intake form on your website. Machine learning-based systems would sort words used in service requests for “plumbing,” “electrical” or “carpentry” in order to eventually route them to the appropriate repair professional. Semantic analysis is a computer science term for understanding the meaning of words in text information.

• Twitter, for example, is a rich trove of feelings, with individuals expressing their responses and opinions on virtually every issue imaginable.
• Emotion analysis is a variation that attempts to determine the emotional intensity of a speaker around a topic.
• By extending the capabilities of NLP, NLU provides context to understand what is meant in any text.

The first step in sentiment analysis is to preprocess the text data by removing stop words, punctuation, and other irrelevant information. Our understanding of the sentiment of text is intuitive – we can instantly see when a phrase or sentence is emotionally loaded with words like “angry,” “happy,” “sad,” “amazing,” etc. ” has considerably different meaning depending on whether the speaker is commenting on what she does or doesn’t like about a product. In order to understand the phrase “I like it” the machine must be able to untangle the context to understand what “it” refers to. Irony and sarcasm are also challenging because the speaker may be saying something positive while meaning the opposite. Much like social media monitoring, this can greatly reduce the frustration that is often the result of slow response times when it comes to customer complaints.

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With the help of machine learning algorithms, it’s possible to hide the inaccuracy and ambiguity of the natural lexicon. For example, people often use oxymorons to add emotion to their comments, but machine learning algorithms can take this into account to produce accurate results of human emotions. The aspect-based analysis is useful in that it helps identify specific topics that people are discussing. The model analyzes our feedback, such as “difficult to use” or “easy product integration”. Based on such phrases it can extract our mood (positive or negative) and, for example, the category in question.

• In this way, the customer can learn about the information and reputation of each place and avoid bad experiences.
• As we can see that our model performed very well in classifying the sentiments, with an Accuracy score, Precision and  Recall of approx 96%.
• NLP technologies further analyze the extracted keywords and give them a sentiment score.
• As such, it is important to allow for some flexibility in the interpretation of the data.
• In many social networking services or e-commerce websites, users can provide text review, comment or feedback to the items.

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