What Is Text Analysis: Text analysis is a process of converting raw data into meaningful information to extract insights from it. This helps all, whether you are a student, marketer, researcher, or business owner. Text analysis helps to understand the given data and make informed decisions. So let’s get started, and in this blog we will dive into what data analysis is, how data analysis works, and also check out the free tools for data analysis.
Table of Contents
What Is Text Analysis?
Text analysis, also called text mining or text analytics, is a method used for extracting meaningful information from unstructured data. Unstructured data may come from various sources, which include social media posts, emails, customer reviews, blog posts, or survey responses. Unlike numerical data, which is stored in the databases, unstructured text data is usually messy and requires specialized techniques to extract information.
High-level tools required to analyze the text, such as machine learning, natural language processing (NLP), and other methods, including statistics like text analysis, uncover patterns, key themes, and sentiments.
Below are some examples:
- Most of the businesses use these tools to analyze the customers’ feedback to improve their products and services.
- Marketers use social media text data to track the brand sentiments.
- Researchers use these tools for extracting the keywords that help to identify the trends.
These text analysis tools transform the chaotic text into meaningful information and structured insights, saving time and revealing the hidden content.
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How Does Text Analysis Work?
Let’s check the steps involved in text analysis: clean, process, and interpret text data.
1. Collecting Text Data
In this steps process starts gathering the text information that has to be analyzed. These sources include
- Customer reviews—collects customer feedback and reviews e-commerce platforms and services.
- Social media—here, collect information through comments on posts and posts.
- Blogs— Collect data from bloggers’ articles available online.
- Emails—Business or individual, both users’ data collected for analysis.
- Interview transcripts or call logs—In this method, we collect the candidate’s transcripts or call logs to analyze their communication skills and matching skills.
- Manual—You can collect the data through copying text and automated API to pull likes and tweets.
2. Cleaning the Data
Mostly raw text data come with irrelevant details, which might include typos, punctuation, or filler words. The cleaning process prepares this data for analysis. Below are the key steps that are included in the cleaning process.
- Tokenization: This cleaning process step splits the data into smaller pieces or words.
- Lowercasing: Making the text into lowercase helps for consistency, such as Google and google being the same word and treated as the same.
- Removing Stop Words: These words contain common words. While cleaning, we filter out the common words like “a,” “the,” or “is” that do not add bigger value in text.
- Stemming/Lemmatization: This cleaning process includes words with root forms such as “jumping becoming “jump.”
- Stripping Special Characters: The cleaning process includes the removal of punctuation, numbers, emojis, and non-relevant characters.
3. Applying Analysis Techniques
In the previous method, the cleaning process was done, and the next step analyze the text using various methods.
- Sentiment Analysis: Using this text analysis method, we analyze the emotions in the text, whether they are positive, negative, or neutral; it helps determine whether a review is positive or negative. and it is crucial.
- Topic Modeling: That technique finds the recurring themes like price and quality in the example customer feedback.
- Keyword Extraction: Important and crucial for SEO and other text analysis, it helps to identify the most frequent keywords and important words.
- Named Entity Recognition (NER): This technique spots the specific details about companies, places, or names, for example, Amazon or Paris.
- Text Classification: This technique categorizes text into various categories, for example, spam vs. non-spam emails.
4. Visualizing Results
Once the collecting of text data and cleaning and analysis techniques are done, the next steps are to display the insights from the data by visualization.
- Word clouds—that show the popular terms from text.
- Pie charts—This displays the text sentiments and breakdown into (e.g., 60% positive, 30% neutral, 10% negative).
- Topic maps—They illustrate the connection between the themes from the text’s insights.
5. Tools and Platforms
The text analysis process is done with high-level programming by using Python with libraries like NLTK or spaCy or by using R programming, but it requires knowledge of programming and experience. There are some tools that can analyze text easily within a few seconds and are free, no coding required, and also accessible for everyone.
Free Text Analysis Tools
There is no need for high-level skills for text analysis like programming, machine learning, deep learning, or a big budget; below are user-friendly tools that analyze the text in the process. With a user-friendly interface, let’s look at these tools one by one.
1. Online Comma Separator
What It Does: This tool converts the lists and columns of data into comma-separated format easily, as well as supporting multiple separators and user specifications, along with various built-in features.
How It Works:
- Copy and paste the list or column data.
- Choose a delimiter for separating the column data, for example, a comma, space, semicolon, etc.
- Click on the convert button and get the ready analysis data for your use.
Use Case: It can help with preparing YouTube SEO tags to blog tags, writing SQL queries, or closing arrays.
Where to Try: The toolifypro.com has its own Online Comma Separator; no need to look for other websites.
Why It’s Great: Simple, user-friendly UI and handles large data sets.
2. Word Counter
What It Does: It counts the words, characters, and sentences and analyzes length and structure. It has more features.
How It Works:
- Simply copy and paste the text data into the tool.
- Display real-time results with stats like word count, character count, or keyword frequency.
- Some features that help in SEO or blog writing.
Use Case: Blogger can use it for word count, checking the word limit for the blog or essay.
Where to Try: We have our own word counter tool, which is better than other tools.
Why It’s Great: The tool has the various features that are usually found in the paid version. This tool is completely free.
3. Excel Formula Bot
What It Does: This is a smart AI-based tool that generates the formulas to analyze the text data using spreadsheets.
How It Works:
- Simply describe your task, for example, (e.g., “split a cell into first and last names”).
- You get the formula for Excel, like =LEFT(A1,FIND(” “,A1)-1), to copy into Excel.
- Now you can apply for analyzing and cleaning your data.
Use Case: Helps to clean text, counts keyword frequency, and records specific words’ data in Excel.
Where to Try: We have our own Excel Formula Bot for free for everyone for unlimited use.
Why It’s Great: It is a smarter way to write formulas for analyzing and cleaning text information in a spreadsheet.
4. CSV Merge Tool
What It Does: While doing the text analysis, there are a lot of CSV files generated whose contents are text information. This tool combines all the files into one for streamlined analysis.
How It Works:
- Simply upload or drag and drop the CSV files.
- Click on merge files.
- Process started immediately and/or based on number of files,
- Download the file.
Use Case: You might use the customers’ feedback list or keywords from multiple sources you can merge using this tool.
Where to Try: Website has its own CSV Merge Tool for free.
Why It’s Great: User-friendly, simple, and handles multiple files.
5. Excel to CSV Converter
What It Does: Some tools might not support Excel files; the user can turn their Excel file into a CSV.
How It Works:
- Upload the Excel files.
- Click on the convert button.
- • The converted file is ready within a few seconds.
- You can download it or use it in databases or other platforms.
Use Case: User can prepare spreadsheet data into a CSV file for Abakysis.
Where to Try: You can use toolifypro.com’s own Excel to CSV Converter tool.
Why It’s Great: This seamless method for data transfer and analysis.
6. Text AI Summarizer
What It Does: This is an AI-based long-to-short text summarizer tool with some more modes.
How It Works:
- Simply paste the text-like article or review data.
- Choose the mode for summary.
- Click on the summarize button and get results.
Use Case: Helps to summarize the feedback, report, or paper for quick results.
Where to Try: You can use this free AI summarizer tool.
Why It’s Great: This tool saves a lot of time and provides a customization feature.
Conclusion
The text analysis process can be a powerful way to take out insights from the unstructured text data, which includes the various steps and processes from text collection to text cleaning and analysis techniques to visualization, as well as free tools for all. Whether you are studying customer feedback, optimizing SEO, or conducting research, the free tools are Online Comma Separator, Word Counter, Excel Formula Bot, CSV Merge Tool, Excel to CSV Converter, and Text Summarizer, which make the text analysis process headache-free without knowledge of coding.
