Data is power in today’s world. With gigabytes of data saved on servers by businesses, everyone is trying to find insights that will benefit the company. There are many instances when statistics are utilized to motivate behaviors, but text classification stands out.
Process of Text Classification
Reading the document into the code is the first step in the text classification procedure. A series of data pre-processing procedures are then chosen based on the business problem:
Tokenization is the process of breaking up lengthy text strings into smaller chunks. For example, the phrase “This sentence needs to be tokenized” will be broken down into the following words:
“This sentence needs to be tokenized,” it says.
Text normalization seeks to level out all of the text content. The following are a few Text normalization options:
Text should be lowercased, and punctuation, tags, and whitespace should be removed.
When a word is stemmed, any affixes (prefixes, suffixes, infixes, or circumfixes) are removed, so studies becomes studi and studying becomes study.
Lemmatization: Obtains the word’s canonical or dictionary form. Studies and studying, for instance, will be changed to study. It is helpful in situations when words must maintain their meaning after being pre-processed.
Stop Word Removal: Elimination of common words like the, and, is, and a that add nothing to the general meaning of the text.
Text sequences are vectorized into numerical features that can be incorporated into the model. Some of the often used methods for the same include TF-IDF and Count vectorizer.
Based on the relevance of the features, feature selection algorithms choose a subset of the features. One of the most popular approaches for feature selection is Document Frequency, which filters away words and features that appear less frequently than a predetermined threshold. In some business contexts, when new features are produced from existing features, feature extraction is an optional step. One option for adding new features is to employ clustering algorithms.
The data must be tagged to predetermined categories using one of the following techniques as the process’s last step:
- Tagging manually
- string-matching methods like fuzzy matching or rule-based filtering.
Several hundred features can be used by learning algorithms like neural networks to tag text material. Two categories of learning algorithms can be made:
Unsupervised learning is used when there is a paucity of data that has already been tagged. To group together related material, strategies like clustering and associating rule-based algorithms might be used. One illustration is the division of customers into groups based on their characteristics, past purchases, and behavior. The patterns found through further analysis of these groups can subsequently be used to personalize consumer approaches.
When there is a sufficient amount of data that has been effectively categorized, supervised learning is used. Based on previously categorized data, the ML algorithms learn the mapping function between the text and the tags. For text classification, algorithms like SVM, Neural Networks, and Random Forest are frequently utilized.
Both the supervised and unsupervised methods of text classification are used in a variety of industries, including social media, marketing, customer experience management, digital media, etc. Use cases for some of them will be provided below.
Learn without supervision:
Unsupervised text classification searches for common themes and structures in texts to classify them together. It finds use in real-world situations where the data volume is too enormous to be fully categorised, where it is happening in real-time, or where the labels are not preset.
CRM Automation Use Case 1: Quicker resolution of customer concerns
To achieve effective customer service, any business that sells products must respond rapidly to client concerns. However, it might take a lot of time and be difficult to do correctly to read every client complaint or comment. The complaints can be categorized into major subjects using unsupervised learning techniques, such as Technical Issues, Subscription-Related Questions, etc., and then assigned to the appropriate teams for resolution. The steps to accomplish this are listed below:
Data extraction and pre-processing are done from client emails or the CRM database. Following tokenization and normalization, the TF-IDF technique is used to extract pertinent features from the data. The data is prepared for the following phase once the features have been extracted and cleaned.
Using a clustering approach, comparable unlabeled texts are gathered into one group (clusters). To aggregate complaints with similar text content, the DBSCAN approach to clustering is used. This approach doesn’t require us to specify the necessary number of clusters because it learns that information based on the initial parameters set, which are eps (the minimum distance between two points to be included in the same cluster) and minPoints (minimum number of points that can form a dense region). The clusters let us estimate how many different types of complaints there are. But identifying and naming the complaint kinds is necessary if we are to learn anything useful from these clusters. This brings us to a different method of unsupervised text classification.
Using topic modeling, a group of texts are sorted into abstract subjects. Latent Semantic Analysis (LSA), TextRank, and Latent Dirichlet Allocation (LDA) are a few examples of topic models (LSA).
Customer complaints are entered into the LDA topic model, which then assigns them a topic. Its foundation is that every document—in this case, the complaint—is a jumble of minor subjects. By calculating the likelihood that the terms in the complaint will appear in specific topics, it accomplishes this. A software company’s client complaints, for instance, might reveal the primary subtopic clusters listed below:
Based on these findings and the fact that Technical, Subscription, Customer Service, and No reply have the largest weights in their clusters, we can classify the themes as Technical Issues, Subscription Issues, Poor Customer Service, No reply, etc. It determines that the words “server is throwing up error on running app” and “app” have the highest likelihood of happening under the heading “Technical Issues” and so categorizes the complaint under that heading.
The customer care staff and the product team can separate these emails once the concerns are marked and then follow up depending on the tags supplied. Additionally, they can develop automatic emails and solutions to frequently occurring problems, which can lessen the workload for customer call executives.
Using Topic Modeling in Use Case 2 for Search Engine Optimization
The majority of today’s most popular websites make use of Topic Clusters to their advantage. A grouping of content under several pertinent themes and subtopics is known as a topic cluster. A website with an analytics theme, for instance, would have the following Topic cluster:
- Overview of cluster content and “Analytic” on the pillar page.
- Cluster Pages: Content for the related clusters of Tools for Data Analysis, Applications of Analytics, and Magazines for Analytics that goes in-depth on each sub-topic within the cluster. Whenever appropriate, linking pages to the pillar page. use of cluster headers and key words to raise page rank on Google.
But it’s harder than it seems to provide pertinent material. This is where artificial intelligence (AI) powered tools may help content marketers. These AI programs’ foundation is made out of unsupervised methods like LDA, clustering, etc. They collect keywords from the marketer’s content pages and search through millions of related web sites for those keywords. Based on this, they offer information on the popular and relevant subtopics, the target market, and the most often asked questions about a topic. With this knowledge, content marketers can generate, change, and upgrade their material much more easily, which will help their page’s Google ranking.
The supervised method of text classification uses material that has previously been categorized or tagged as training data for the models. Compared to unsupervised learning techniques, this method has higher accuracy and scaling potential.
Use Case 1: Gathering information about a new product from posts on social media
According to statistics, 3.8 billion individuals utilize social media worldwide, or 49% of the total population.
Therefore, it is obvious that companies today have a significant social media presence. It is a tool used by businesses to market their names and products, find and target new clients, gather public opinion about their brands, goods and services, rivals, etc. Companies frequently use social media post analysis to discover how the general public feels about their newly launched products. They use supervised learning techniques to gain insights because it can be tedious to read every single message.
Contextual Semantic Search is used to determine which product aspects have received the greatest attention after data extraction from social media posts and pre-processing (CSS). It uses inputs like price, feature, customer service, and user accessibility and filters the postings according to the appropriate principles. Sentiment analysis can then offer information on how people feel about these issues generally.
Sentiment analysis can help determine whether the general public has a favorable, unfavorable, or neutral opinion of the new product and its features. The algorithms used to determine the overall sentiment of the post can either be rule-based (looking for the presence of specific words that are grouped as positive/negative/neutral) or machine learning (ML)-based, which uses previously tagged posts to train on and predict the final sentiment on posts. A benchmark can be obtained by conducting a sentiment analysis on a competitor’s social media post.
Spam Out is a Use Case 2
There is a spam folder in every mail account. Gmail is a vivid illustration of how spam is categorized. In February 2019, Google implemented an AI-based system based on Tensorflow to identify spam. As a result, it was able to recognize and isolate an extra 100M spams every day as opposed to the first rule-based approach. In order to classify a message as spam, the rule-based technique looked for the existence of specific terms. On the other side, the AI used previously classified spam mails to uncover patterns that helped it detect spam that the rule-based technique could not detect.
There are numerous further applications for text analytics, including chatbots, language-based chat routing, and classification of online store products. What unites all of the use cases is text classification’s capacity to boost workflow effectiveness, cut down on human labor, and produce data-driven insights. The application cases for Text classification will expand as a result of the exponential rise in technology, machine learning, and artificial intelligence. This will increase the potential for analytics to address real-world issues.