Text classification is the process of categorizing text into specific groups. By using Natural Language Processing (NLP), text classifiers automatically analyse text and set predefined tags or categories based on its content.  If the data cannot be assigned to specific cateogories, but the data is still analysed or processed automatically, the technique of keyword extraction is more suitable than text classification. Read here how you can use keyword extraction to find the most important topics in your customer feedback.

Large volumes of unstructured text are being uploaded and sent around the world every minute of every day. It is hard to extract valuable information from this type of data unless it is organized in a way that can identify the important topics being discussed that are relevant for your business. Doing this manually requires a lot of staff and is time-consuming, generating high costs. However, automated text analysis tools that use text classifiers with NLP can structure vast amounts of textual data and analyse it in a cost-effective and scalable manner.

Text is unstructured data and is found in every organisation. Read this article about unstructured data. Whether in the form of communication between employees, with customers, or in documents. Analysing, organising and sorting text data is difficult and time-consuming, so most organisations do not realise its full potential. 

Text classification can help companies make use of all the unstructured text and help them gain valuable insights. Using text classifiers businesses can automatically structure all sorts of texts, e-mails, legal documents, social media, chatbots etc. in an efficient and cost-effective way. This allows companies to save time and make smart data-driven evidence based decisions. 

For example, support tickets can be automatically assigned to the right contact persons and company departments, documents can be structured according to content in a database, or emails can be channelled correctly.

1. Scalability
   Manual research and organisation is inefficient and less precise. Machine learning can evaluate millions of comments, tweets, emails etc. at a fraction of the costs usually within minutes depending on the size of the data being analysed. 
   

2. Real-time analysis
   There are emergency situations where businesses need to identify the issue as quickly as possible and take prompt action (e.g., PR crisis on social media). Brand monitoring is a useful tool that uses text classification to follow your brand mentions, so you can identify vital information and take action right away. 
   

3. Consistent criteria
   Human employees will make errors in classifying text data due to distractions, stress and boredom, and human subjectivity produces inconsistent norms. Machine learning however applies the same lens and parameters to all data and outcomes. Once a text classification model has been adequately trained, it performs with impeccable accuracy.

Most of the many approaches to automatic text classification can be categorised into three types of systems:

1. Rule-based systems

2. Machine learning-based systems

3. Hybrid systems

Rule-based systems

Rule-based approaches classify text into organised groups by using a set of linguistic rules. These rules direct the system to use semantically relevant text elements to classify relevant categories on the basis of their content. Each rule consists of a pattern and a predicted category. 

For example, imagine you want to classify magazine articles into two groups: Music and Movies. First you have to define two lists of words that characterise each group (e.g. words related to music such as Cello, orchestra, symphony, pop, RnB, rock etc. and words related to movies like thriller, drama, Michael Bay, Brad Pitt, comedy etc.). For classification, you can now define rules such as a certain number of keywords from one of the two categories or a certain ratio of words contained in the two categories. By means of these rules, the articles can then be assigned to the respective category.

For example, this rule-based system will classify the headline “Sydney orchestra to perform in a free charity concert” as Music because it counted two music-related words (orchestra and concert) and didn’t count any movie-related words. 

Rule-based systems are comprehensible for humans and can be improved over time. However these systems are very time consuming, limited to the dictionary of words given by the user and it can be challenging to generate rules for a complex system and maintain them. They also require the user to have a deep understanding of the classification task at hand. In our example, a broad knowledge of the two categories of music and film must be available and, most importantly, created in a glossary.

Machine learning based systems

Machine learning text classification learns to classify based on past observations. By using pre-labeled examples as training data, machine learning algorithms can learn the different relationships between parts of texts and that a specific output (i.e. tag) is required for a particular input (i.e., text). The “tag” is the predetermined classification or category that any text might fall under. 

One of the first steps in training a machine learning NLP classifier is feature extraction: a method is used to transform each text into a quantitative representation in the form of a vector. Bag of words is one of the most frequently used approaches, where a vector represents the frequency of a word in a predefined dictionary of words. This model is only concerned with whether known words occur in a document, any order or structure is discarded. 

Once the machine is trained with enough samples, the machine learning model can start to make accurate predictions. Text classification with machine learning is more accurate and faster than manual rule-based systems. The classifiers are easier to maintain because new examples can always be tagged to learn new tasks. 

Hybrid Systems

Hybrid systems integrate a machine-learning root classifier with a rule based system used to further enhance performance. These systems can be easily specified by adding rules for tags that have been incorrectly modeled by the root classifier. Hybrid systems combine the advantages of rule-based and machine learning-based text classification. By defining rules, sensitive assignments can be clearly defined and the handling of false positives and false negatives can be improved. The effort involved in labelling data is also significantly reduced by the combination. This is why Cauliflower also relies on hybrid approaches for classification tasks. Learn more about the technology behind Cauliflower here.

Conclusion

Text classification can be the perfect tool for developing state-of-the-art structures and managing company knowledge. Transforming text data into quantitative data is extremely helpful in generating valuable insights and driving business decisions. Through the automation of manual and routine tasks, time can be reoriented towards other important business operations.