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What is Sentiment Analysis?

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                  Sentiment analysis means contextual data mining wherein you input a sentence, and it is categorized according to the underlying consumer emotions. A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase. When we look at any sentence, the human brain searches for sentiment-bearing phrases – that is, words and phrases that carry a tone or opinion and tries to interpret it, usually as adjective-noun combinations. We also draw from our previous experiences and accumulated knowledge to identify each sentiment-bearing phrase and interpret their negativity or positivity. This is precisely how computer sentiment analysis works. It involves deep learning and machine learning techniques that “trains” the system to instinctively recognize nouns and phrases as “offensive” and categorize them accordingly.
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Why topic modeling?

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  As large amounts of data are collected every day, more and more information becomes available. At the same time, it becomes difficult to access the necessary information that we are looking for. Moreover, as these kinds of data are unstructured or free-form text, analyzing such volumes of text data manually becomes highly tedious and time-consuming. The simple solution is to use Topic modeling , as it provides us with methods for automatically organizing, understanding, searching, and summarising extensive electronic archives. It can help us sort through unstructured data in the following ways: - Discovering the hidden themes in the collection. - Classifying the documents into the discovered themes. - Using the classification to organize/summarise/search the documents. With the use of Topic Modeling , we can figure out what topics a bunch of unstructured text cover. This set of text documents may range from emails to survey responses, support tickets, product reviews, etc. Once we id
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why sentiment analysis is important

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                What are the benefits of using effective Sentiment Analysis? Using free online sentiment analysis, one can gauge how their customers feel about different business areas without reading thousands of customer comments at once. The techniques and tools used by  Textrics  enable a company to drill down into different customer segments of the business and get a better understanding of sentiment in these segments. With the help of our tool, market research processes can become much more straightforward. You can also improve customer satisfaction, discover new marketing strategies, improve media perceptions and crisis management, increase sales revenue, and so much more. How Can Sentiment Analysis be Used? Sentiment Analysis finds a variety of applications within an organization to understand the voice of customers and employees. It plays a significant role for any business or organization. It helps data analysts within large enterprises gauge public opinion, conduct nuanced
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how does topic modeling work

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  Two types of Topic Modeling Algorithms ? There are several algorithms for doing topic modeling. The most popular ones include: Latent Semantic Analysis(LSA) Latent Semantic Analysis, or LSA, is one of the crucial foundation techniques in topic modeling. We can use it for text summarization, text classification, and dimension reduction. It is similar to the cosine similarity. As for LSA, we develop a matrix using the words present in the document’s paragraphs in the corpus. The matrix rows will represent the unique words present in each section, and columns represent each paragraph. Latent Dirichlet Allocation (LDA) The Latent Dirichlet Allocation (LDA) & LSA are based on the same underlying assumptions: the distributional hypothesis, (i.e. similar topic makes use of similar words) and the statistical mixture hypothesis (i.e. documents talks about several topics) for which a statistical distribution can be determined. The motive of LDA is to map each document in our corpus to a s
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what is topic modelling

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  Have you ever been in a situation where you were asked to find a specific piece of information for your company, from an extensive collection of documents, within a tight deadline? When you are looking through the company database manually for a crucial piece of information, it is highly time-consuming and practically impossible. With the growing amount of data in recent years, it is difficult to obtain the relevant and desired information in a short period, especially during urgent matters. In such cases, we can use  Topic Modeling  to mine through the data and fetch the information we are looking for quickly. It automatically identifies topics present in a text object and derives hidden patterns exhibited by a text corpus. Thus, assisting better decision making. A good topic model should result in the following– “health”, “doctor”, “patient”, “hospital” for a topic such as “Healthcare”. For a business that deals with thousands of customer interactions daily, such as social media, c
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what is sentiment analysis in r

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                              Sentiment analysis means contextual data mining wherein you input a sentence, and it is categorized according to the underlying consumer emotions. A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase. When we look at any sentence, the human brain searches for sentiment-bearing phrases – that is, words and phrases that carry a tone or opinion and tries to interpret it, usually as adjective-noun combinations. We also draw from our previous experiences and accumulated knowledge to identify each sentiment-bearing phrase and interpret their negativity or positivity. This is precisely how computer sentiment analysis works. It involves deep learning and machine learning techniques that “trains” the system to instinctively recognize nouns and phrases as “offensive” and categorize them a
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