Exploratory Data Analysis Techniques for Unstructured Data Anushree Shinde

Exploratory Data Analysis Techniques for Unstructured Data  Anushree Shinde

Unstructured data, which includes written documents, social media postings, photos, audio files, and video files, is data that does not already have a predetermined format or organisation. Unstructured data analysis might be difficult, but there are a number of EDA techniques you can use to draw conclusions from such data. Here are some methods:

1. Data Cleaning and Preprocessing: Unstructured data frequently needs intensive preparation and cleaning. In order to do this, tasks including deleting superfluous data, standardising formats, addressing missing data, and getting rid of noise or inconsistencies must be completed.

2. Text Mining and Natural Language Processing (NLP): When working with textual data, NLP approaches can aid in the extraction of valuable information. Tokenization, stemming, lemmatization, deleting stop words, and part-of-speech labelling are a few of these methods. To find patterns and insights, NLP technologies like sentiment analysis, topic modelling, and named entity recognition can be employed.

3. Image and Video Processing: EDA techniques can be used to perform operations like image segmentation, object detection, feature extraction, and content-based image retrieval on unstructured data in the form of photos or videos. These methods can assist with pattern recognition, image classification, and information extraction from visual data.

4. Dimensionality Reduction: High-dimensionality unstructured data can make it challenging to analyse and visualise. The data can be reduced to a lower-dimensional form using dimensionality reduction techniques like Principal Component Analysis (PCA) or t-SNE, making it simpler to study and analyse.

5.Visualization: The use of visualisations in EDA for unstructured data is essential. You may see patterns, connections, and clusters in the data by using methods like word clouds, scatter plots, bar charts, heatmaps, and network graphs.

6. Statistical Analysis: In order to find patterns, trends, or groups in your unstructured data, you can use statistical techniques like frequency analysis, distribution analysis, or clustering algorithms.

Your choice of specific methods and strategies will be influenced by the properties of your unstructured data and the objectives of your research. Before using EDA methodologies, it's crucial to have a firm grasp on your data and the issue you're trying to solve.

πŸ‘Anushree  Shinde  [ MBA] 

Business Analyst

10BestInCity.com Venture





LinkedIn: https://www.linkedin.com/in/anushree-shinde20

Facebook: https://shorturl.at/hsx29

Instagram: https://www.instagram.com/10bestincity/

Pinterest: https://in.pinterest.com/shekharcapt/best-in-city/

Youtube: https://www.youtube.com/@10BestInCity

Email: info@10bestincity


#EDA , #ExploratoryDataAnalysis

#DataExploration , #DataAnalysis

#UnstructuredData , #TextAnalysis

#NLP , #SentimentAnalysis

#TopicModeling , #TextMining

#DataVisualization , #DataCleaning

#FeatureExtraction , #DataPreprocessing

#DataInsights , #DataDiscovery

#PatternRecognition , #DataPatterns

#DataPatternsDiscovery , #DataPatternsAnalysis

No comments:

Post a Comment