Trending MTech Natural Language Processing (NLP) Projects
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MTECH NLP Project List with Abstract and Source Code - 21/22
| 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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