NLP Project List with Abstract and Source Code - 21/22
An attribute of computer science called Natural Language Processing (NLP) falls under the category of Artificial Intelligence. The main purpose of NLP is to make computers understand spoken words in the same way that humans do. The use of NLP is a key factor of pc applications that translate texts among languages, reply to spoken commands, and summarize big volumes of text hastily – even in real time – for the benefit of customers. You can have become acquainted with NLP through voice-activated gps systems, digital assistants, speech-to-textual content dictation software, and customer support chatbots. As well as streamlining commercial enterprise operations, increasing employee productiveness, and simplifying task-essential business procedures, nlp has advanced into a developing a part of company solutions.
We have selected quite interesting projects in Machine Learning & Artificial Intelligence and tried to explain each project in detail with step by step execution where every individual irrespective of their educational background can learn and execute this projects by themselves.
Project List with Step by Step execution
| S.No | Project Code | NLP Projects | Action |
|---|---|---|---|
| 1 | TYTNLP1001 | Conversational Chatbot using ML and NLP with Front End Framework | DETAILS |
| 2 | TYTNLP1002 | Resume Parser using Spacy NLP Library with Streamlit Integrated | DETAILS |
| 3 | TYTNLP1003 | Sentiment Analysis of a people using Tweets made by user about Airlines | DETAILS |
| 4 | TYTNLP1004 | Converting words to numbers using gensim word2vec (amazon cell phone and accesories dataset) | DETAILS |
| 5 | TYTNLP1005 | Converting words to numbers using gensim word2vec (sports and outdoors dataset) | DETAILS |
| 6 | TYTNLP1006 | Fake News Classification using LSTM | DETAILS |
| 7 | TYTNLP1007 | Movie Recommendation Engine with Streamlit Web Framework | DETAILS |
| 8 | TYTNLP1008 | Building a Chatbot (Add intents as your wish) | DETAILS |
| 9 | TYTNLP1009 | Sentiment analysis: Develop a system to classify text as positive, negative, or neutral sentiment. | DETAILS |
| 10 | TYTNLP1010 | Text classification: Build a model to classify text into various categories (e.g. spam vs. non-spam, news articles by topic). | DETAILS |
| 11 | TYTNLP1011 | Part-of-speech tagging: Create a system to identify and classify the parts of speech in a given sentence. | DETAILS |
| 12 | TYTNLP1012 | Named entity recognition: Develop a model to extract and classify named entities (people, organizations, locations, etc.) from text. | DETAILS |
| 13 | TYTNLP1013 | Machine translation: Build a system to translate text from one language to another. | DETAILS |
| 14 | TYTNLP1014 | Text summarization: Create a model to automatically generate a summary of a long document. | DETAILS |
| 15 | TYTNLP1015 | Dialogue generation: Design a system that can generate appropriate responses in a conversation. | DETAILS |
| 16 | TYTNLP1016 | Text generation: Build a model that can generate original text in a specific style or on a particular topic. | DETAILS |
| 17 | TYTNLP1017 | Chatbot development: Create a chatbot that can carry out natural language conversations with users. | DETAILS |
| 18 | TYTNLP1018 | Text simplification: Develop a system to simplify complex text for easier comprehension. | DETAILS |
| 19 | TYTNLP1019 | Sentence completion: Build a model that can predict the next word or phrase in a sentence. | DETAILS |
| 20 | TYTNLP1020 | Textual entailment: Create a system to determine whether a given text is entailed by another text. | DETAILS |
| 21 | TYTNLP1021 | Sentiment analysis for social media: Develop a model to classify the sentiment of social media posts. | DETAILS |
| 22 | TYTNLP1022 | Stylometry: Build a system to identify the author of a given text based on their writing style. | DETAILS |
| 23 | TYTNLP1023 | Sentiment analysis for stock market prediction: Create a model to predict stock price movements based on sentiment analysis of financial news articles. | DETAILS |
| 24 | TYTNLP1024 | Spam detection: Develop a system to identify spam messages in a corpus of text. | DETAILS |
| 25 | TYTNLP1025 | Sentiment analysis for movie reviews: Build a model to classify movie reviews as positive or negative sentiment. | DETAILS |
| 26 | TYTNLP1026 | Text-to-speech synthesis: Create a system that can convert text to speech in a natural sounding voice. | DETAILS |
| 27 | TYTNLP1027 | Speech recognition: Build a model to transcribe spoken language into text. | DETAILS |
| 28 | TYTNLP1028 | Language identification: Develop a system to identify the language of a given text. | DETAILS |
| 29 | TYTNLP1029 | Part-of-speech tagging for low-resource languages: Create a model to classify the parts of speech in a sentence in a low-resource language. | DETAILS |
| 30 | TYTNLP1030 | Sentiment analysis for customer reviews: Build a model to classify customer reviews as positive or negative sentiment. | DETAILS |
| 31 | TYTNLP1031 | Sentiment analysis for political speeches: Develop a system to classify political speeches as positive, negative, or neutral sentiment. | DETAILS |
| 32 | TYTNLP1032 | Text classification for fake news detection: Create a model to identify fake news articles. | DETAILS |
| 33 | TYTNLP1033 | Text generation for social media: Build a model that can generate social media posts in a specific style or on a particular topic. | DETAILS |
| 34 | TYTNLP1034 | Text generation for creative writing: Develop a system to generate original creative writing prompts. | DETAILS |
| 35 | TYTNLP1035 | Sentiment analysis for product reviews: Create a model to classify product reviews as positive or negative sentiment. | DETAILS |
| 36 | TYTNLP1036 | Sentiment analysis for election prediction: Build a model to predict election outcomes based on sentiment analysis of news articles and social media posts. | DETAILS |
| 37 | TYTNLP1037 | Text classification for hate speech detection: Develop a system to identify hate speech in text. | DETAILS |
| 38 | TYTNLP1038 | Sentiment analysis for brand reputation management: Create a model to classify the sentiment of social media posts about a particular brand. | DETAILS |
| 39 | TYTNLP1039 | Sentiment analysis for sports commentary: Build a model to classify the sentiment of sports commentary. | DETAILS |
| 40 | TYTNLP1040 | Text generation for news articles: Develop a | DETAILS |
| 41 | TYTNLP1041 | Text generation for weather forecasts: Build a model that can generate natural language weather forecasts. | DETAILS |
| 42 | TYTNLP1042 | Sentiment analysis for email classification: Create a system to classify emails as positive, negative, or neutral sentiment. | DETAILS |
| 43 | TYTNLP1043 | Sentiment analysis for political news articles: Develop a model to classify political news articles as positive, negative, or neutral sentiment. | DETAILS |
| 44 | TYTNLP1044 | Text classification for product categorization: Build a model to classify products into various categories. | DETAILS |
| 45 | TYTNLP1045 | Text classification for legal document classification: Create a system to classify legal documents into various categories. | DETAILS |
| 46 | TYTNLP1046 | Text classification for job posting classification: Develop a model to classify job postings into various categories. | DETAILS |
| 47 | TYTNLP1047 | Text classification for resume classification: Build a model to classify resumes into various categories. | DETAILS |
| 48 | TYTNLP1048 | Text classification for research paper classification: Create a system to classify research papers into various categories. | DETAILS |
| 49 | TYTNLP1049 | Text classification for customer support ticket classification: Develop a model to classify customer support tickets into various categories. | DETAILS |
| 50 | TYTNLP1050 | Text classification for support request classification: Build a model to classify support requests into various categories. | DETAILS |
| 51 | TYTNLP1051 | Text classification for news article classification: Create a system to classify news articles into various categories. | DETAILS |
| 52 | TYTNLP1052 | Text classification for marketing email classification: Develop a model to classify marketing emails into various categories. | DETAILS |
| 53 | TYTNLP1053 | Text classification for spam email classification: Build a model to classify spam emails. | DETAILS |
| 54 | TYTNLP1054 | Text classification for phishing email classification: Create a system to classify phishing emails. | DETAILS |
| 55 | TYTNLP1055 | Text classification for fraudulent email classification: Develop a model to classify fraudulent emails. | DETAILS |
| 56 | TYTNLP1056 | Text classification for legal document analysis: Build a model to classify legal documents as relevant or not relevant to a particular case. | DETAILS |
| 57 | TYTNLP1057 | Text classification for medical document classification: Create a system to classify medical documents into various categories. | DETAILS |
| 58 | TYTNLP1058 | Text classification for financial document classification: Develop a model to classify financial documents into various categories. | DETAILS |
| 59 | TYTNLP1059 | Text generation for product descriptions: Build a model that can generate natural language product descriptions. | DETAILS |
| 60 | TYTNLP1060 | Sentiment analysis for social media posts about a particular topic: Develop a model to classify the sentiment of social media posts about a particular topic (e.g. a particular brand, political issue, etc.). | DETAILS |
| 61 | TYTNLP1061 | Text classification for topic classification: Create a system to classify texts into various topics. | DETAILS |
| 62 | TYTNLP1062 | Text generation for customer support responses: Build a model that can generate natural language responses to customer support inquiries. | DETAILS |
| 63 | TYTNLP1063 | Text classification for sentiment analysis of social media posts in multiple languages: Develop a model to classify the sentiment of social media posts in multiple languages. | DETAILS |
| 64 | TYTNLP1064 | Text classification for sentiment analysis of customer reviews in multiple languages: Create a model to classify the sentiment of customer reviews in multiple languages. | DETAILS |
| 65 | TYTNLP1065 | Text classification for sentiment analysis of product reviews in multiple languages: Build a model to classify the sentiment of product reviews in multiple languages. | DETAILS |
| 66 | TYTNLP1066 | Sentiment analysis for customer service inquiries: Develop a system to classify customer service inquiries as positive, negative, or neutral sentiment. | DETAILS |
| 67 | TYTNLP1067 | Text classification for sentiment analysis of news articles in multiple languages: Create a model to classify the sentiment of news articles in multiple languages. | DETAILS |
| 68 | TYTNLP1068 | Text classification for spam detection in multiple languages: Build a model to identify spam messages in multiple languages. | DETAILS |
| 69 | TYTNLP1069 | Text generation for social media posts in multiple languages: Build a model that can generate social media posts in multiple languages. | DETAILS |
| 70 | TYTNLP1070 | Text classification for hate speech detection in multiple languages: Develop a system to identify hate speech in multiple languages. | DETAILS |
| 71 | TYTNLP1071 | Text classification for fake news detection in multiple languages: Create a model to identify fake news articles in multiple languages. | DETAILS |
| 72 | TYTNLP1072 | Text generation for creative writing prompts in multiple languages: Build a model that can generate creative writing prompts in multiple languages. | DETAILS |
| 73 | TYTNLP1073 | Sentiment analysis for customer reviews in multiple languages: Develop a model to classify customer reviews in multiple languages as positive or negative sentiment. | DETAILS |
| 74 | TYTNLP1074 | Text classification for product categorization in multiple languages: Create a system to classify products into various categories in multiple languages. | DETAILS |
| 75 | TYTNLP1075 | Text classification for legal document classification in multiple languages: Build a model to classify legal documents into various categories in multiple languages. | DETAILS |
| 76 | TYTNLP1076 | Text classification for job posting classification in multiple languages: Develop a model to classify job postings into various categories in multiple languages. | DETAILS |
| 77 | TYTNLP1077 | Text classification for resume classification in multiple languages: Create a model to classify resumes into various categories in multiple languages. | DETAILS |
| 78 | TYTNLP1078 | Text classification for research paper classification in multiple languages: Build a model to classify research papers into various categories in multiple languages. | DETAILS |
| 79 | TYTNLP1079 | Text classification for customer support ticket classification in multiple languages: Develop a system to classify customer support tickets into various categories in multiple languages. | DETAILS |
| 80 | TYTNLP1080 | Text classification for support request classification in multiple languages: Create a model to classify support requests into various categories in multiple languages. | DETAILS |
| 81 | TYTNLP1081 | Text classification for news article classification in multiple languages: Build a model to classify news articles into various categories in multiple languages. | DETAILS |
| 82 | TYTNLP1082 | Text classification for marketing email classification in multiple languages: Develop a model to classify marketing emails into various categories in multiple languages. | DETAILS |
| 83 | TYTNLP1083 | Text classification for spam email classification in multiple languages: Create a system to classify spam emails in multiple languages. | DETAILS |
| 84 | TYTNLP1084 | Text classification for phishing email classification in multiple languages: Build a model to classify phishing emails in multiple languages. | DETAILS |
| 85 | TYTNLP1085 | Text classification for fraudulent email classification in multiple languages: Develop a model to classify fraudulent emails in multiple languages. | DETAILS |
| 86 | TYTNLP1086 | Text classification for legal document analysis in multiple languages: Create a system to classify legal documents as relevant or not relevant to a particular case in multiple languages. | DETAILS |
| 87 | TYTNLP1087 | Text classification for medical document classification in multiple languages: Build a model to classify medical documents into various categories in multiple languages. | DETAILS |
| 88 | TYTNLP1088 | Text classification for financial document classification in multiple languages: Develop a model to classify financial documents into various categories in multiple languages. | DETAILS |
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