Latent semantic analysis for text categorization using neural network

• Basic semantic units are semantic units that cannot be replaced by other semantic units.
• Natural language analysis is a tool used by computers to grasp, perceive, and control human language.
• Finally, customer service has emerged as an important area for sentiment research.
• A technology such as this can help to implement a customer-centered strategy.
• The age of getting meaningful insights from social media data has now arrived with the advance in technology.
• This paper presents the concept of Neural Network, work done in the field of NN and Natural Language Processing, algorithm, annotated corpus and results obtained.

One last caveat is that the size of the chunk of text that we use to add up unigram sentiment scores can have an effect on an analysis. A text the size of many paragraphs can often have positive and negative sentiment averaged out to about zero, while sentence-sized or paragraph-sized text often works better. These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.

Now, we can understand that meaning representation shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relation and predicates to describe a situation. The most important task of semantic analysis is to get the proper meaning of the sentence. For example, analyze the sentence “Ram is great.” In this sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram. That is why the job, to get the proper meaning of the sentence, of semantic analyzer is important.

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