AI and Text Analysis: Best Approaches To Follow
AI and text analysis help businesses overcome this challenge and make the most of this opportunity. This article will cover the most used techniques for it.
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Artificial intelligence and text analysis provide you with a deep understanding of your business’s performance and customers, empowering you to make better decisions.
From automating repetitive tasks to delivering actionable customer insights, AI helps businesses to improve revenue and user experience. Similarly, text analysis interprets extensive collections of data to uncover consumer trends and opportunities.
Text analytics refers to the method of analyzing a text to extract useful, high-quality information. Around 80-90% of the data in every organization is unstructured. Text analysis uses AI and ML technologies to generate valuable insights, which you can use to make data-driven decisions.
The tremendous amount of data generated each day provides businesses with an opportunity and a challenge.
The opportunity: It allows companies to get in-depth insights on your customer’s opinions about your products or services.
The challenge: Processing a vast amount of data and generating valuable information from them.
Text analysis helps businesses overcome this challenge and make the most of this opportunity.
Text Analysis Techniques
Previously, text analysis was performed manually, which involved using keyword dictionaries and identifying recurrent terms. As a result, companies had to wait for months before getting actionable insights.
Thanks to the advancement in technologies, you can now process a massive amount of data in no time. Here are the techniques that are used in text analysis.
Artificial Intelligence (AI): Artificial intelligence refers to the technology that imitates human behavior concerning the intelligence processes involved in problem-solving.
Natural Language Processing (NLP): A part of AI, NLP enables the computer program to review and understand human languages. Text analysis uses NLP to eliminate the noise from unstructured data to help you understand customers’ opinions about your business and identify trends.
Machine Learning (ML): A subset of AI, ML, can automatically learn from past experiences and improve itself without any manual intervention. ML categorizes new pieces of data by analyzing how the old ones were processed.
Deep Learning (DL): A part of machine learning, DL can process and use data to better understand the context in the unstructured data, thereby improving the accuracy of automated analysis of the text.
Sentiment Analysis: It refers to the ability of a computer system to determine how customers feel about your business, products, or services (positive, neutral, or negative).
Approaches to Text Analysis
There are various methods to gather actionable insights from the unstructured data. These approaches use the technologies mentioned above to convert invaluable data into valuable ones.
Let’s take a look at the approaches to text analysis.
Dependency parsing refers to the process of analyzing the grammatical structure of a sentence. It uses dependency grammar to determine the relationships between “head” words and phrases that modify those heads. The link between the terms gives you further details about the sentence.
For example, in the sentence, “his speech about marshmallows in New York is utter bullshit,” there are two noun modifiers (marshmallow and speech). The word “utter” is an adjective that modifies the noun “bullshit.”
Key Takeaway: Dependency parsing helps identify the relationship between words, which enables you to determine the sentiment of the users in the text.
Constituency parsing breaks sentence structure using context-free grammars. It divides sentences into constituents, i.e., sub phrases that belong to a specific category in the grammar.
For example, a constituency parse tree for the sentence “I saw a fox” looks like this.
As you can see, constituency parse divides the sentence into two parts, i.e., NP (noun phrase) and VP (verb phrase). It means the English grammar rule states that a sentence can be created with the concatenation of a noun (I) and a verb phrase (saw a fox).
Similarly, the verb phrase is further divided into two categories (verb and noun phrase).
Constituency parsing creates trees (like the one mentioned above) and divides the sentences into single phrasal components to determine its syntactic structure.
Key Takeaway: Constituency parsing enables you to understand the syntactic complexity of the text you want to analyze.
Stemming and Lemmatization
Stemming refers to the process of trimming words to bring them to their root form. For example, stemming algorithms reduce the terms argue, argued, argues, and arguing to the stem argue.
Lemmatization, on the other hand, determines the lemma of a word depending on its intended meaning. Unlike stemming, lemmatization uses a complex morphological analysis and dictionaries to select the correct lemma based on the context. For example, the lemmatization algorithm reduces the words argue, argued, argues, and arguing to argue.
Key Takeaway: Both stemming and lemmatization help train a system with data and clean the noise from the text.
Text classification refers to the method of assigning predefined categories to unstructured texts. Text classification makes it easier to do sentiment analysis, detect spam, and the intent behind the text.
Take this sentence as an example, “the headphones have great sound quality.” A classifier can take this as input and assign relevant tags, such as “headphones” and “sound quality.”
Text classification is used to:
Determine customer sentiment
Understand what a given text is talking about
Detect the intent within texts
Extract important insights, such as keywords, features, etc.
Identify the meaning of a word within the sentence.
Key Takeaway: Text classification can help you organize and categorize texts faster in a cost-effective way.
Cross-validation is a way to assess the predictive performance of text classifiers in situations where the data is limited. The primary aim of cross-validation is to see how the text analysis model works (in terms of accuracy) when you have a new set of data. It is also used to train the program to improve its efficiency in future text analysis.
For example, consider this sentence, “this app has a great UI and is easy to use.” Divide it into four subsets with 25% of the sentence each. It will look like this:
This app has
A great UI
And is easy
Input the first three subsets into the classifier to predict texts in the fourth subset. Check if the model predicted it correctly. If not, use more sentences like this to train the program and increase accuracy.
Key Takeaway: Use cross-validation to test the performance of your text analysis model and improve its accuracy.
6- Regular Expression
A regular expression is a series of characteristics used to define a search pattern. Regular expressions are usually used for three tasks:
Find texts within a large set of data.
Validate if a string adheres to the desired format.
Replace or insert text.
For example, you can leverage a regular expression to extract phone numbers or email addresses from massive unstructured text content.
Key Takeaway: Leverage regular expression to find texts that matter the most to your business.
Text analysis is much easier now than it was a few years ago. When used the right way, it can help you gain useful, actionable insights to make data-driven decisions. Use the six approaches mentioned in the article to save time, automate tasks, and offer a great user experience to your customers.
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