A Guide to Text Analytics and Sentiment Analysis

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Today’s world is all about data, and understanding the customer through data has become a critical aspect of business success.  

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Companies are increasingly focusing on extracting meaningful insights from vast amounts of customer-related data to enhance their decision-making processes, improve customer experiences, and gain a competitive edge.  

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With the explosion of digital content, companies are increasingly looking towards developing new ways to extract insights from an ocean of unstructured online data in the form of digital content.

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Text analytics and sentiment analysis are methods employed to process and analyze natural language text data to extract insights and understanding from the data.

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This guide provides an overview of text analytics and sentiment analysis, and something companies should practice before their competitors do. We’ll dive into the definitions, techniques, applications, and the limitations that come along with using text analytics and sentiment analysis.

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What is Text Analytics?

Analyzing textual data like emails, social media interactions, and chats can be difficult due to its unstructured nature. Manually sorting through text is both time-consuming and costly, leading to the need for extra personnel to complete this.

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Text analytics is a machine learning technique of extracting meaningful insights from raw unstructured text to identify a common theme. It lets you understand the meaning behind the text so you can see what the topic of conversation is.

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Text analytics is like sorting and reading a giant pile of letters and sorting them into different piles based on their content. This would mean the letters about ‘products’ come in one pile and ‘customer experience’ in another. This allows the company to see patterns and understand what people are writing or talking about online.

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Companies use text analytics tools like Ascribe to quickly absorb web data and documents and turn them into useful insights.

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What is Sentiment Analysis?

If we used the same metaphor of reading the letters and sorting them, sentiment analysis would mean reading those letters and sorting them based on the writer's emotions.

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Sentiment analysis evaluates a text's sentiment by examining its polarity (positive, negative, neutral), emotions (angry, happy, sad, etc.), urgency (urgent, not urgent), and intent (interested, not interested). If the emotion in the text is happy, the data is labeled as positive and negative if the emotion is upset/unhappy.

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Since sorting the emotion comes after data categorization, this is a subfield of text analytics. The tones measured in this are positive, negative, and neutral. This helps businesses understand the overall sentiment of a larger audience towards a specific product or service and helps them make data-driven decisions.

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Also known as opinion mining or emotion AI, sentiment analysis utilizes natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study emotional states and subjective information.

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Companies can use sentiment analysis to monitor customer feedback and track changes in customer sentiment over time. Evaluating a product, down to its specific quality, can be monitored with sentiment analysis. It’s always better to know what the customer feels and then bring about a change to improve on identified shortcomings in your business or service.

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The Many Applications of Text and Sentiment Analytics

A business can improve and become more customer-centric when working with text analytics and sentiment analysis.  

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They can use these tools to identify areas where they need to improve quickly. Major areas companies gain benefit in are:

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1. Monitoring Online Brand Reputation - Text analytics and sentiment analysis tools use natural language processing to examine customer comments, tweets, and social media discussions, offering insights into their feelings. This helps companies respond more empathetically to consumers. Additionally, by looking at how clients interact on different platforms, companies can adjust their approach to suit different audience reactions.

2. Market Research - Understanding specific trends, products, or services can help a company make informed decisions about pricing, product development, and marketing strategies. Sentiment analysis offers a unique market perspective, revealing valuable insights into consumer perceptions and data polarity. This research can address various market trends, from predicting oil price drops due to political instability to forecasting popular shows for an OTT content platform in specific regions.

3. Customer Segmentation - Categorizing customers based on their attitudes and preferences can help brands better target specific customer groups more effectively and personalized marketing campaigns.

4. News Articles - The news revolves around public opinion. By analyzing news articles, companies gather the public’s sentiment on specific issues and use this information to make better-informed decisions.

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What are the Steps Involved in Text and Sentiment Analytics?

In understanding the results of text and sentiment analytics, it’s essential to know the steps involved in processing the data. These steps are:

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1. Data Collection - The first step in text analytics and sentiment analysis is collecting data from various sources, such as customer feedback, social media, and surveys.

2. Data Preprocessing - After data collection, the data is cleaned and formatted for analytical purposes.

3. Leveraging NLP (Natural Language Processing) - Doing this helps convert unstructured text data into a structured, easy-to-analyze format. Some standard NLP techniques in this step include tokenization, stemming, lemmatization, and part-of-speech tagging.

4. Text Mining - The text is further filtered by extracting meaningful information from the text data. This includes keyword extraction and document classification.

5. Sentiment Analysis - Rule-based algorithms, machine learning, and deep learning help to further classify the tone of the data and categorize it into sentiments.

6. Data Visualization – Visualizing this data through PowerBI and Tableau brings data into charts, graphs, and maps to make the data more insightful for better decision-making.

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The techniques used for text analytics and sentiment analysis involve text mining, natural language processing (NLP), and machine learning. With these techniques, companies can extract large amounts of meaningful data, providing insights into customer behavior, preferences, opinions, surveys, and more.

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Meanwhile the tools and technologies employed for text analytics and sentiment analysis include programming languages such as Python, NLP libraries, and machine learning libraries such as scikit-learn.

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OpenText, IBM Watson, and Sentiment140 provide comprehensive tools and technologies for text analytics and sentiment analysis.

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In tandem with these tools, Tableau and PowerBI can be used to visualize the analysis results and make them more understandable and actionable.

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The Limitations of Text Analytics and Sentiment Analysis

While the potential is there for text and sentiment analytics, there are certain limitations that must be addressed for it to be a well-rounded solution.

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The technology is still learning and trying to keep up with the vast amounts of data it gets to process. The effectiveness still needs to catch up in certain places where inaccurate results are forced with slang, emoticons, and sarcasm in content, further complicating the process.

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The forever looming question of privacy comes into play again with these enhanced techniques. Much of the data extracted contains personal and financial information. This sensitive data should remain protected and confidential to maintain the privacy and security of individuals.

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Human supervision is critical for removing errors or biases found in insights drawn by the NLP algorithms. Final decisions and results need to be validated by human experts.

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At the end of the day, these techniques will make the marketing world increasingly dependent on data more than ever. Gaining insights into customer behavior, preferences, and opinions is a gold mine for companies that now have precisely what they need to improve their products, services, or brand image.

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The Bottom Line

Ultimately, companies can only grow by deeply understanding their customers. This is crucial as human experiences involve complex emotions and relationships. Artificial Intelligence can help us classify these emotions and set benchmarks for using emotional intelligence as a key metric.

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Text and sentiment analysis have become essential tools in the era of AI and ML algorithms.

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No matter the size or scope of your business data, these powerful techniques can be used to interpret the information. By leveraging text and sentiment analysis, organizations can gain valuable insights, improve customer experiences, and make data-driven decisions, driving their success in today's data-centric world.

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