In the information age in which we live, the huge proliferation of texts generated daily represents a great challenge for those who seek to draw relevant information.
However, thanks to the power of Topic Modeling, text analysis becomes a smooth and efficient process.
In this way, Topic Modeling is an important resource for companies and professionals who want to explore and understand text content efficiently and effectively.
What is Topic Modeling?
Topic Modeling is a text analysis technique that identifies the main “topics” present in a set of documents without requiring manual labeling.
The goal of Topic Modeling is to detect hidden themes in documents and automatically assign texts to these themes. In other words, the Topic Modeling allows you to group documents according to the common themes that characterize them.
Use cases
The Model Topic has several practical applications in various industries.
- Research: Topic Modeling can be used to analyze large amounts of scientific articles. This allows researchers to gain a detailed overview of emerging topics, identify the most relevant publications, and focus their research efforts more specifically.
- Customer feedback analysis: by applying the Topic Modeling on feedback data, it is possible to automatically identify the main topics discussed by customers, such as prices, customer service, product quality, etc. This allows companies to gain a comprehensive overview of strengths and areas for improvement, enabling them to make targeted decisions to improve products and services, establish a more effective dialogue with customers and maintain a competitive advantage in the market.
- Media Monitoring: this approach allows you to get a detailed overview of trends, relevant topics, and discussions involving the media. For example, you can identify current issues, such as politics, economics, the environment, sports or entertainment, and analyze how these issues evolve over time.
These examples represent only some of the possible applications of Topic Modeling. Its versatility allows it to be adapted to many contexts in which the analysis of texts and the identification of the underlying themes are crucial to extract insights and benefit from the huge amount of textual data available.
How does Topic Modeling work?
Topic Modeling uses machine learning models. One example is the popular LDA (Latent Dirichlet Allocation), which treats each document as a mixture of arguments and each argument as a mixture of words.
The Topic Modeling process can be divided into several steps:
1. Data Extraction: Data relevant to a particular company, product, brand or service can be extracted from:
- Internal sources: company information acquired by email, surveys, customer support, databases, etc…
- External sources: information acquired by social media, newspaper news, online reviews, forums, etc…
In this case you can take advantage of technical specifications, such as the “Web Scraping” (that is, the extraction of data from the web).
2. Data pre-processing: once collected, the texts must be prepared for analysis.
Among the most common steps you can mention:
- Cleaning: remove special characters, stop-words (words of negligible value such as “e”, “il”, “un”…), punctuation and everything that, depending on the case, is irrelevant for the analysis;
- Tokenization: Break down texts into smaller units (“tokens”). A practical example is the transformation of a sentence into a list of the words contained in it;
- POS (Part-of-speech) tagging: assign a grammatical category (such as name, verb, adjective and adverb) to each token;
- Lemmatization/Stemming: reduce each token to its original basic form, considering more the very root of the word (in this way, words like “elaboro”, “elaborate” and “processed” will be grouped)
3. Model Building: this phase involves the actual training and learning of the model.
4. Interpretation of the results: once the model has been trained and the results of the analysis have been extracted, they are represented and explained through an intuitive and informative dashboard.
This may include identifying the main themes, analysing the keywords associated with each topic, and visually representing the results using graphs or concept maps.
Conclusions
In conclusion, the Topic Model represents a significant step in the analysis of texts, allowing you to detect the underlying themes in documents in an automated way. Using machine learning algorithms, this technique offers a wide range of applications in different industries, allowing you to extract relevant information from large quantities of texts. With the increasing availability of textual data, Topic Modeling becomes increasingly crucial for organizing information and gaining valuable insights from texts. In summary, the Topic Model provides an important resource to understand, analyze and exploit the informative potential of texts.
Blue BI and Topic Modeling
Blue BI, who has always believed in the value of data, helps companies use Topic Modeling to identify the main themes of documents, allowing them to gain valuable information and make strategic decisions.
If you want to know more, contact us!
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