Imagine that you need to find a specific piece of information for your company, from a large collection of documents, within a tight deadline. It may be an important statistic or a crucial piece of information needed urgently. How much time do you think it will take for you to find that information from your company database? Looking for information within millions of documents will be like looking for a needle in a haystack. It will take a long time and the task will be practically impossible. This is where Machine learning and Topic modeling comes into place.
What is topic modeling?
Large amounts of data, such as emails, research documents, product reviews, and news articles are collected by organisations every day. With this amount of data being collected, accessing information becomes difficult. It is not possible for a person to single-handedly go through vast quantities of information to search for a particular topic or understand meanings in a group of documents.
To understand a collection of documents such as news articles or blog posts, we need to separate them into different categories. Such categorisation of documents into "topic" is called Topic modeling.
As per the name suggests, a topic model will give you the ‘topics’ and a body of words that go together and make sense in a set of documents. Simply put it's about making sense of collection of documents by dividing them into different topics. It is a machine learning tool to organise, search, and understand large amounts of information.
So, why is topic modeling useful and what kind of problems does it solve?
It uncovers hidden patterns in a collection of documents
It organises documents according to topics
In a collection of text documents such as emails, surveys, support tickets, etc, Topic modeling divides them into natural groups and organises them by different topics. Making it easier to find them.
Examples of topic modeling:
Topic modeling can help with finding and tracking topics in a text stream and creating topic hierarchies. It can create a hierarchy of topics that shows the possible relations between them. Therefore, being able to analyse high quality topics in a text stream.
Sentiment analysis is an important technique of web analysis and information retrieval. Topic modeling, in the capacity of sentiment analysis, can help with identifying opinions and sentiment extraction, with a large amount of user-generated content on the web, such as social media, blogs, forums, etc.
Question and answers
Text analysis is gaining a lot of interest because of unstructured data such as questions and answers, articles, comments, etc. They can be used to identify and predict people’s opinions from unstructured text data. Topic modeling can help with the generation of various question topics, and then classifying new questions according to the relevant topics.
How does topic modeling work?
Topic modeling, which mirrors the normal usage of natural language. where every document is seen as a mixture of topics. The model suggests that every word in the document is connected to one of the topics.
How topic modeling works here is that it assumes each document is made of a mixture of topics and tries to understand what kind of presence every topic has in each document. The documents are grouped together based on the words, and the algorithm notices the connection between them.
The main assumption of this technique is that documents of the same topic use similar keywords. And that every document is made of a mixture of topics, and there is a chance of every word belonging to a certain topic.
Topic Modeling Use Cases
With the rapid increase in data, there is an urgent need of machine learning techniques that can automate data analysis. Topic modeling, a subset of Natural Language Processing, has been getting a lot of attention in recent years because of its insightful approach to text data, and efficiency with repetitive manual work. With its usage in areas such as social media monitoring, brand awareness and customer service, companies are taking advantage of Topic modeling and its effectiveness.
Here we review some of the current applications of Topic modeling:
Topic Modeling can be used to monitor brand reputation through social media, blogs, review sites and forums. Analysing information on a day to day basis is quite important for a brand. This helps in maintaining a brand’s reputation. The importance of brand reputation can be known from the fact that 90% of consumers form opinions on a business based on their online reviews. 60% of consumers use products and services only if it has 4 or more stars. Considering this, Topic modeling can keep a check on brand image by tracking the business areas (topics) that consumers are discussing the most. Sentiment analysis can be used to know customer opinion and Keyword extraction can reveal the most relevant topics of conversation, giving a deeper insight of customer opinions.
Sales and Marketing
The purpose of sales and marketing is lead generation. And identifying leads is a difficult task, requiring tons of research and other manual work. Machine learning can automate some of these tasks, making them more effective and time saving. Topic learning models can help predict quality of leads based on their company descriptions. It can also be used to identify potential customers by analysing emails and identifying if the customer is a good fit.
Outbound email responses can be filtered with the help of ML models, to avoid sending unwanted emails and allowing receivers to opt out according to their response. This saves the sales team a lot of time.
Improving Customer Experience
A great customer experience is important to make a business stand out from its competitors. Customer service plays a major role in influencing consumer loyalty towards a product or service. According to statistics 56% of consumers stop using a product due to poor customer service.
Clearing support tickets at help desk play a big role in customer service. It involves processing huge amounts of texts. Here, machine learning algorithms can be used to sort out data, such as customer support tickets, to understand the issue related with each ticket by recognising text patterns.
A researcher’s ability to interpret biological information is highly improved with the help of Topic modeling. In the past decade, there has been an increasing growth of biological data. This brings in the requirement of extracting information and relations from such data. Topic modeling has become a useful tool in bioinformatics, as researchers are implementing the technique into various biological data. It helps in understanding the biological meaning of topics in more detail.
Topic modeling and text analysis is giving companies valuable insights from their data. With the help of machine learning it is now possible to gain deep knowledge about consumers, their likes and dislikes, online conversations, etc. This provides companies opportunities for further improvements and helps them be prepared for future crises.