Semantic Analysis and Deep Learning – MMISU

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Semantic Analysis and Deep Learning

The various steps involved in text processing or data processing, such as tokenization, lemmatization, word embedding, and tf-idf. Sentiment Analysis is a technique that uses Natural Language Processing , Text Mining, and Computational Linguistics to identify and extract the emotions present in the text. Semantic technology defines and connects information by developing languages to express rich and self-descriptive interrelationships of data in a form that machines can process and store. There have also been huge advancements in machine translation through the rise of recurrent neural networks, about which I also wrote a blog post. With a sentiment analysis API, you can mine bodies of text to extract sentiment with ease.


There are different semantic analysis machine learninges proposed towards sentiment analysis, which are broadly categorized into rule-based, aspect-based and ML-based approaches. However, exploiting rule-based approaches in conjunction with machine learning has resulted in more efficiency. This section primarily discusses some of the key literature pertaining to sentiment analysis. Carvalho & Plastino provide literature study of feature representation in Twitter sentiment analysis. Categorizing features that have comparable characteristics, the authors used feature selection algorithms to find relevant subsets of features in each dataset. Lexicon based approaches have been applied as an unsupervised method to perform sentiment analysis (Hu et al., 2013).

Sentiment analysis models

In other words, we can say that polysemy has the same spelling but different and related meanings. In this task, we try to detect the semantic relationships present in a text. Usually, relationships involve two or more entities such as names of people, places, company names, etc.

What does a semantic analysis do?

What is Semantic Analysis? Semantic analysis is the task of ensuring that the declarations and statements of a program are semantically correct, i.e, that their meaning is clear and consistent with the way in which control structures and data types are supposed to be used.

It uses linguistic analysis to determine the root form of a word, and it is necessary to have a comprehensive dictionary for the algorithm to reference in order to link the word form to its root. This process can help to improve the accuracy and performance of machine learning models by reducing the number of variations of a word and making the text more structured. Tokenization is the process of breaking down a sentence into individual words, known as tokens. These tokens are used to understand the context of the sentence and to create a vocabulary.

Deep Learning andĀ Natural Language Processing

The positive and negative score of the identified tweet is then calculated. If the positive score exceeds the negative score, the tweet is considered positive; otherwise, it is considered negative. As a feature extraction algorithm, ESA does not discover latent features but instead uses explicit features represented in an existing knowledge base. As a feature extraction algorithm, ESA is mainly used for calculating semantic similarity of text documents and for explicit topic modeling. As a classification algorithm, ESA is primarily used for categorizing text documents.

What is semantic analysis in NLP?

Semantic analysis then examines relationships between individual words and analyzes the meaning of words that come together to form a sentence. This analysis provides a clear understanding of words in context.

Majority voting ensemble is the ensemble technique and we also choose a best trained ensemble which is explained below. In the last few years, soft computing and internet technologies have emerged as an inevitable tool for business. NLP has given rise to a new paradigm called sentiment analysis which is also known as opinion mining. It is the management of sentiments, views, and subjective material (Yeole, Chavan & Nikose, 2015). Sentiment analysis deals with the process of analyzing several tweets and reviews to provide comprehensive information on public opinion. It is a tried-and-tested tool for predicting a wide range of key events, including boxing matches, movie box office receipts and general elections (Heredia et al., 2016).

Intra-model performance assessment

These models can be further improved by training on not only individual tokens, but also bigrams or tri-grams. This allows the classifier to pick up on negations and short phrases, which might carry sentiment information that individual tokens do not. Of course, the process of creating and training on n-grams increases the complexity of the model, so care must be taken to ensure that training time does not become prohibitive.

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If a synset has non-zero score for all three categories, it means that the corresponding tweet has each of the three sentiment-related properties to some degree. If it is zero for a particular category, then the tweet is very clearly positive or negative accordingly. Natural language processing is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more.

Significance of Semantics Analysis

Search autocompleteā€˜ functionality is one such type that predicts what a user intends to search based on previously searched queries. It saves a lot of time for the users as they can simply click on one of the search queries provided by the engine and get the desired result. It understands the text within each ticket, filters it based on the context, and directs the tickets to the right person or department (IT help desk, legal or sales department, etc.). The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle. The platform allows Uber to streamline and optimize the map data triggering the ticket. The approach helps deliver optimized and suitable content to the users, thereby boosting traffic and improving result relevance.

majority voting ensemble

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