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Sentiment Analysis

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  Sentiment analysis, also known as opinion mining, is a natural language processing technique for determining whether textual data is positive, negative, or neutral. While data growth is unavoidable, data value remains a function of analytical quality. Among many explanatory fields, one in which humans outperform all others is the ability to recognize feelings. Traditional methods of gauging popular sentiment, tracking brand and product reputation, analyzing customer experiences, and understanding the market are being rapidly replaced by sentiment analysis tools. Manual sentiment anal y sis is also possible; simply read each piece of feedback and determine whether it is positive or negative. However, for a small number of feedback presented to you, such as  40–50  or even  100 , this is doable. However, if you have a data set of, say,  10,000  reviews, manually analyzing them becomes impractical. Not to mention the time and bias that will ensue. In the future, sentiment analysis will
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Semantic Similarity

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  Semantic similarity   is an important aspect of Natural Language Processing and one of the fundamental problems for many NLP applications and related disciplines. Semantic Textual Similarity can be described as a measure used to a set of documents with the goal of determining their semantic similarity. The similarities between the documents are based on their direct and indirect linkages. The existence of semantic relations among them can be used to measure and recognize these linkages. Many semantic web applica t ions, such as community extraction, ontology building, and entity identification, benefit from semantic similarity. It is also beneficial for Twitter searches, where the ability to reliably quantify semantic relatedness between concepts or entities is necessary. One of the primary difficulties in information retrieval is retrieving a set of documents and finding images by captions that are semantically connected to a particular user query in a web search. Benefits of Semant
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Text Analysis in Retail

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  The  retail business  operates in an extremely complex marketing and sales landscape, with platforms ranging from traditional stores to e-commerce websites the latter of which is rapidly rising. Day after day, more consumers turn to eCommerce platforms to acquire their items or, at the very least, to gather other consumers’ thoughts before visiting a store most daily users read reviews before making a purchase decision. While access to what customers are  s aying is no longer difficult, finding the time to read, analyze, understand, and categories that data is nearly impossible — especially when firms attempt to do so with information from many data sources. This is where text analytics comes in. As a result, data is being generated at an unprecedented rate, and its significance is expanding. This type of data can be analyzed manually as long as a process is in place, but it becomes incredibly difficult to conduct when bigger subsets of data are involved, particularly those from diff
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Text Analysis in Food Industry

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  The food sector is gradually growing worldwide, with operators and manufacturers accounting for the vast majority of the global market share. The food industry as a whole includes all businesses that serve meals for immediate consumption, such as restaurants, cafeterias, and catered shops. Food product developments, personalization, and increased demand for healthy meals among target groups are all effectively contributing to the expansion of the foodservice space. Restaurants are eager to adopt new technologies in order to deliver better, more personalized service. One of the most sought-after options is text analytics. The unstr u ctured text contains more than 80% of the total information. Text analytics can help you meet your consumers’ expectations by allowing you to easily analyze large amounts of text data. The text analysis solution assisted the foodservice client in assessing customer views of the brand. The client was also looking for techniques to evaluate customer sentime
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