Key Differences between Sentiment Analysis and Text Analytics

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Key Differences between Sentiment Analytics and Text Analytics Kiosk Surveys
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Introduction

Sentiment analysis and text analytics are two words that are frequently used synonymously in the field of data analytics. Although each of these tools are used to draw inferences based on information, their objectives are fundamentally different. For this reason, anyone who is trying to get the most out of their data must be aware of the key distinctions between these two methods. Keep reading to find out!

Text Analytics

What is Text Analytics? 

Analyzing text-based unstructured data is a process known as text analytics, or text mining. The main objective of text analytics is to transform unstructured data into structured data that can be quickly analysed and interpreted. Natural language processing (NLP), statistical analysis, and machine learning techniques are used in text analytics to extract insights from text data. 

At its core, text analytics is a branch of NLP that deals with the evaluation and processing of textual information. Sentiment analysis, language translation, speech recognition, and chatbot development are just a few of the many uses for NLP and text analytics.

Text analytics has become a crucial tool for organisations, researchers, and governments looking to learn insightful information and make informed decisions. As the volume of text data collected each day increases, text analytics will continue to be crucial in many fields. Yet it’s crucial to comprehend text analytics’ difficulties and use the right strategies to get over them.

Applications of Text Analytics

There are many uses for text analytics in many different industries. Here are some of its typical applications: 

1. Sentiment Analysis: Sentiment analysis is the act of locating and classifying the opinions expressed in text, such as news stories, consumer reviews, and social media posts. Companies employ sentiment analysis to learn what their customers think of their products and brand. 

2. Topic Modeling: Topic modelling is the technique of determining the themes that are present in a text. This is helpful in a range of situations, like as news stories, academic journals, and social media posts. 

3. Named Entity Recognition: The process of identifying entities in text, such as persons, locations, and organisations, is known as named entity recognition (NER). This is helpful in many situations, such as monitoring social media, analysing news, and creating legal documents. 

4. Text classification: Text classification is the process of dividing a text into predetermined groups. This is helpful in a number of areas, including content filtering, sentiment analysis, and spam identification.

Sentiment Analysis

What is Sentiment Analysis

The process of analysing and evaluating people’s feelings and opinions about a certain topic, brand, product, or service is known as sentiment analysis, often referred to as opinion mining. It is a way for companies and individuals to learn more about how their clients feel and what they think of their goods and services. 

The main objective of sentiment analysis is to identify and analyse the attitudes, sentiments, and emotions that are expressed in online reviews, social media posts, and other types of writing. Using natural language processing (NLP) and machine learning techniques, the sentiment analysis algorithm categorises text as positive, negative, or neutral based on the words and phrases used in the text. 

By examining the words and phrases used in a document, sentiment analysis algorithms often ascertain the overall sentiment of the content. The likelihood that the content will be categorised as positive increases when words like “love,” “wonderful,” and “great” are included. The likelihood that the content will be categorised as negative will also rise when words like “hatred,” “disappointing,” and “frustrating” are used.

Sentiment analysis has developed into a key instrument that organisations leverage to understand the opinions and feedback of their customers. In the upcoming year, we can anticipate the emergence of more advanced sentiment analysis methods as the field of natural language processing develops. 

Check our blog why sentiment analysis is important for your business. 

Applications of Sentiment Analysis

There are numerous uses for sentiment analysis across numerous sectors. Some of the most popular sentiment analysis applications include the following: 

1. Customer service: Teams providing customer service can use sentiment analysis to quickly detect and resolve client complaints and difficulties. 

2. Brand reputation management: Sentiment analysis can help companies keep an eye on their online reputation and analyse how customers view their brand. 

3. Product development: Sentiment analysis can help companies understand the wants and desires of their customers by analysing feedback on existing products or services. 

4. Political campaigns: Sentiment analysis can be used to gauge public attitude during political campaigns.

5. Financial analysis: Investor and trader sentiment can be examined using sentiment analysis in the financial markets.

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Differences between Sentiment Analytics and Text Analytics

1. Focus of Sentiment Analytics and Text Analytics

Text analytics and sentiment analysis are both techniques for drawing conclusions from textual data. Their focus, however, is different. While text analytics is a broader term that includes a wide range of techniques used for evaluating textual data, such as text classification, topic modelling, and entity recognition, sentiment analytics, as the name suggests, is focused on studying the sentiment represented in a piece of text. 

2. Data Sources for Sentiment Analytics and Text Analytics

The data sources for sentiment analytics and text analytics can be similar, but sentiment analytics typically focuses on data sources where sentiment is expressed explicitly, such as social media posts, product reviews, and customer feedback. Text analytics, on the other hand, can work with any type of textual data, including emails, customer support logs, and legal documents.

3. Methods used for Sentiment Analytics and Text Analytics

In order to assess sentiment, sentiment analytics generally uses methods like lexicon-based analysis, machine learning, and natural language processing. Contrarily, text analytics use a wider variety of methods, such as text preparation, feature extraction, and clustering.

Sentiment Analysis vs Text Analytics: Which One to Choose?

Both sentiment analysis and text analytics are well-liked methods for studying text data. But which one ought to you pick? Text analytics and sentiment analysis are two methods for drawing conclusions from textual data. Sentiment analysis is concerned with identifying the text’s emotional tone and categorising it as positive, negative, or neutral. On the other side, text analytics entails utilising NLP algorithms to extract information from unstructured data. This can involve activities like topic modelling, entity recognition, and summarization. 

Depending on the precise objectives of your analysis, you may want to choose between sentiment analysis and text analytics. Sentiment analysis is the best method for determining how people feel about a certain subject. Text analytics, however, would probably be more helpful if you want to extract specific data or insights from a vast amount of unstructured text. In the end, both approaches can yield insightful information, and the optimal course of action will depend on your particular use case.

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