{"id":10877,"date":"2021-09-15T13:59:20","date_gmt":"2021-09-15T13:59:20","guid":{"rendered":"https:\/\/www.revuze.it\/?p=10877"},"modified":"2021-09-15T13:59:20","modified_gmt":"2021-09-15T13:59:20","slug":"what-is-the-difference-between-text-mining-and-sentiment-analysis","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/what-is-the-difference-between-text-mining-and-sentiment-analysis\/","title":{"rendered":"What Is the Difference Between Text Mining and Sentiment Analysis?"},"content":{"rendered":"
A few years back, data was a vogue word; but things have dramatically changed. We are now in the era of big data; most businesses depend on data for their daily transactions and decision-making.<\/span><\/p>\n A Forbes article reports that the amount of data created, captured, and copied in 2020 reached 59 trillion gigabytes; an almost whopping <\/span>5,000%<\/span><\/a> departure from the 1.2 gigabytes of 2010. While a large volume of data is created and downloaded daily, it\u2019s important to note that the vast majority of the data we can find online is unstructured.<\/span><\/p>\n Data that can be used for business purposes and decision-making must be in a structured format, and this is where the problem lies, as most of the data out there is not structured. Technology is advancing at a very rapid speed and with tools such as text mining and sentiment analysis, the problem of structuring and analyzing large volumes of data is now automated.<\/span><\/p>\n Text mining<\/b><\/a>, or text data mining, is the <\/span>process of transforming unstructured text into a structured format<\/b>; having <\/span>80%<\/span><\/a> of data in the world residing in an unstructured format, text mining enables you to identify meaningful patterns and new insights.<\/span><\/p>\n On the other hand, <\/span>sentiment analysis<\/b><\/a> \u2014 or opinion mining\u00a0 \u2014 <\/span>leverages natural language processing (NLP) to classify data or reviews into positive, negative, or neutral sentiments<\/b>.<\/span><\/p>\n While the two processes might appear to be similar, there is a world of difference between them. But first, it\u2019s necessary to understand what data formatting is before exploring the differences between text mining and sentiment analysis.<\/span><\/p>\n The essence of exploiting text mining and sentiment analysis is to make better business decisions. Advanced analytical techniques, such as Na\u00efve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, are enabling organizations to discover hidden relationships and make better sense of their unstructured data.<\/span><\/p>\n It\u2019s not uncommon to have people mix up the terms text mining and <\/span>text analytics<\/span><\/a>. <\/span>While text mining is extensively used to derive qualitative insights from unstructured text, text analytics is used to provide quantitative results<\/b>. You can use text mining to understand if a customer is happy with your product through the analysis of reviews and surveys.\u00a0<\/span><\/p>\n To have a deeper insight such as identifying a pattern like a negative spike in customers\u2019 experiences or trends, you use text analytics.<\/span><\/p>\n Virtually every company or organization has a website today. Customers visit websites to source products and services; they leave large volumes of data on their trail through the visits and actions they perform online. <\/span>Web analytics enables you to collect, report, and analyze website data<\/b>.<\/span><\/p>\n However, you need to integrate text mining and sentiment analysis to make useful sense out of the data that you gather from your website. Data from most visitors are usually unstructured; text mining will be used to structure the data, while you deploy sentiment analysis to understand the real significance and nuances in the data.<\/span><\/p>\n With this, you can determine the success or failure of those goals, have a data-driven strategy and improve the user\u2019s experience.<\/span><\/p>\n Let\u2019s take a look at the main differences between text analytics and sentiment analysis:<\/span><\/p>\n A lot of activities go into text mining; these activities are essential for the deduction of useful information from unstructured data. You, however, must begin with text processing for the cleaning and transformation of data into a usable format.<\/span><\/p>\n Tokenization, part-of-speech tagging, language identification, chunking, and syntax parsing are necessary steps for proper data formatting before you can embark on the actual analysis. After the completion of text processing, you then proceed with text mining algorithms for veritable insights from your data.\u00a0<\/span><\/p>\n Some common techniques you can use for text mining techniques include:<\/span><\/p>\n Information retrieval is the automated process that responds to a set of predefined queries or phrases to enable the return of relevant information or documents. IR systems can accomplish this task by using algorithms to track user behaviors and discover any data that is relevant.\u00a0<\/span><\/p>\n Library catalog systems and search engines such as Google make use of information retrieval.\u00a0<\/span><\/p>\n Some tasks you can use IR to execute include:<\/span><\/p>\n Natural language processing (NLP) is that branch of <\/span>artificial intelligence (AI)<\/span><\/a> that gives computers the ability to understand the text and spoken words the way humans do. By combining computational linguistics with statistical, machine learning, and deep learning models, NLP enables computers to use these technologies to process text or voice data with a clear understanding.<\/span><\/p>\n\n
Text mining vs. text analytics<\/b><\/h2>\n
The relationship between web analytics, text mining, and sentiment analysis<\/b><\/h2>\n
The differences between text mining and sentiment analysis<\/b><\/h2>\n
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\n Text mining<\/b><\/td>\n Sentiment analysis<\/b><\/td>\n<\/tr>\n \n What it does: <\/b>Shows what has been written by customers about your product or service; what ideas are commonly linked in the text. It also shows which subjects and topics are most discussed by users and customers.<\/span><\/td>\n What it does: <\/b>Allows you to understand if your customers are reviewing your products or service positively, negatively, or neutrally. You can even go beyond non-text feedback, such as video, audio, and images. When a customer smiles, you can easily understand that the customer is satisfied compared to when a customer frowns.<\/span><\/td>\n<\/tr>\n \n How it can help you: <\/b>Helps identify early warnings as an indication that your organization is heading into troubled waters or that there is an issue with your product or service.<\/span><\/td>\n How it can help you: <\/b>Negative scores indicate that your customers are on the verge of churning your product or service.<\/span><\/td>\n<\/tr>\n \n How it works: <\/b>A patented NLP technology processes text-based data just like the human brain, but this is done with proprietary algorithms to identify parts of speech, words, or ideas that are linked, and comprehensively determine patterns and trends in your database.<\/span><\/td>\n How it works: <\/b>The focus is on determining whether words and phrases are positive, negative, or neutral. This is mostly done on a scale of -1 to +1, where -1 is extremely negative and +1 is absolutely positive.\u00a0\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Popular text mining techniques<\/b><\/h2>\n
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Information retrieval (IR)<\/span><\/h3>\n
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Natural language processing (NLP)<\/span><\/h3>\n