1 / 18

Text analysis - Everything you need to know

Text mining tools can help you automate the analysis of large volumes of text data. This can drastically increase efficiency and help you make data-driven decisions. We hope you consider BytesView to explore the possibilities of analyzing textual data<br><br>

BytesView
Télécharger la présentation

Text analysis - Everything you need to know

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Text Analysis Everything you Need to Know

  2. What is Text Analysis? Text analysis is the process of compiling, analyzing, and extracting valuable insights or information from large volumes of unstructured texts, using machine learning and NLP (natural language processing) techniques.

  3. Text Analysis Process

  4. 1. Text Cleanup The text cleanup process involves getting rid of any unwanted information from the compiled textual data. The process removes information such as ads from web pages, unwanted symbols, or standardizing text converted from the binary format.

  5. 2. Tokenization This process involves splitting the text into white spaces. For example: Just do it. The sentence results in 3 tokens, ‘just-do-it’. Each word is a token. Simply put, it breaks raw sentences into words or sentences known as tokens that help you understand the context and interpret the meaning by analyzing the sequence of the words.

  6. 3. Part-of-speech (POS) tagging The POS tagging involves labeling every word in a sentence with the right part of speech. The part of speech includes verbs, nouns, adverbs, adjectives, pronouns, conjunction, etc.

  7. Text Analysis Techniques

  8. Topic labeling Sentiment Analysis Feature Extraction Sentiment analysis, also known as opinion mining, is a text analysis model that can analyze and interpret the sentiments expressed by the author in any piece of text. Feature extraction is a text analysis model that specializes in extracting important features or product facets from large volumes of unstructured text data. Topic labeling is a text analysis technique that can help you categorize and understand large volumes of textual data based on the theme of the information source

  9. Intent Detection Semantic Similarities Entity Extraction Semantic similarity is a text analysis technique that analyzes the likeliness of two pieces of text having the same or similar meaning The entity extraction text analysis model enables you to extract entities from any piece of text. The named entities are then classified as per pre- defined codes, and much more. Intent detection is a machine learning and natural language processing technique that can help you automate the classification of text based on intent

  10. Industry Applications of Text Analysis

  11. Social Media Management Over 80% of brands and B2B marketers use social media platforms to promote their brand. But, if you have customers on social media platforms, you must analyze what they are talking about your brand. Text analysis voice of customers solution can help you analyze and understand the opinions, reviews, complaints, etc, related to your brand.

  12. Customer Support Customer support is another area that is being highly influenced by text analysis. Customer support teams get numerous queries, complaints, inquiries, etc, regularly. Intent detection, a text analysis technique helps classify these incoming tickets in real-time based on the intent of the author.

  13. Business Intelligence Analyzing large volumes of data is an irreplaceable aspect of gathering business intelligence. Numbers can be easy to analyze, but manually analyzing text data can be too time- consuming and inefficient. Over 80% of data on the internet is available in text. Also, most of any business’s data is available in text.

  14. Journalism Journalism is another sector that is being highly influenced by text analysis. Journalists have to verify the legitimacy of any news before deciding to publish it. To do so, they have to analyze a heck ton of information and understand all facets of a story.

  15. Healthcare Doctors often keep notes of clinical data. These notes account for approximately 80% of all clinical data produced. However, this is not part of the EHR (Electronic Health Record). Now, doctors and physicians can use speech-to-text devices to keep an electronic record of all cases. This data can be later used as and when needed.

  16. Finance Text analysis tools can benefit a lot of industries, and the financial sector is one of them. The financial sector involves banking, credit, insurance, trading markets, mergers, acquisitions, and much more. These activities produce a lot of unstructured data that is difficult to even sort, let alone examine. Also, examining all of this data is vital to carry out daily operations.

  17. Close the deal and move in. Congratulations on your new home! Text analysis tools can help you automate the analysis of large Ask your realtor about special arrangements. volumes of text data. This can drastically increase efficiency and With Canva Presentations, you can collaborate in real-time and feel like you’re in the same room as your teammates or co-presenters. help you make data-driven decisions. We hope you consider BytesView to explore the possibilities of text analysis. Schedule moving day. Don't forget the keys. With real-time collaboration, share tasks and work simultaneously to create a powerful presentation.

  18. Thank You

More Related