Text Analytics: Need of the hour
Text Analytics
Text analytics has gained lots of traction in these days. As humungous amount of data is generated in textual form, especially from social media applications, it has become imperative to use these data for gaining valuable insights, especially in the field of business analytics.
steps
Text analytics involves various processes to extract meaningful insights from unstructured text data. Here are the seven steps typically involved in text analytics:
- Data Collection: First, collect text data from documents, social media, emails, surveys, etc. This data can be structured or raw text.
- Preprocessing: Data must be cleansed and preprocessed before analysis. Remove irrelevant information (e.g., stop words, punctuation), convert text to lowercase, tokenize, and stem or lemmatize.
- Text Parsing: Preprocessed text is parsed to determine syntactic structure and word relationships. This can employ part-of-speech tagging, named entity recognition, and dependency parsing to grasp text grammar.
- Text data is converted into numerical representations for machine learning algorithms during feature extraction. Bag-of-words, TF-IDF, word embeddings (e.g., Word2Vec, GloVe), and n-grams are common text analytics feature extraction methods.
- Modelling: After extracting features, machine learning or statistical models can analyse text data and provide conclusions. Sentiment analysis, topic categorization, document clustering, regression, and sequence modelling are examples of these models.
- Evaluation: After developing the models, examine their performance to see if they can complete the task. Depending on the job, evaluation measures may include accuracy, precision, recall, F1-score, and perplexity.
- Finally, text analytics results must be analysed and visualised. Charts, graphs, and dashboards can be used to visualise insights and explain or summarise findings.
These steps are iterative and may require revisiting earlier stages based on the results obtained during later stages or feedback from domain experts.