NLP 😇 Project and ideas to implement - technilesh
10. Data Collection 11. Music Emotion Recognition
11. Data Collection 11. Music Emotion Recognition With data collection, we can analyze user behavior across many websites to find out the best times to ask users for their information to target them with relevant ads. Data collection is a good project if you're already interested in websites and online advertising but don't have an in-depth understanding of computer science. 12. Social Network Analysis 12. Social Network Analysis With social network analysis, we can analyze how users interact with the online world through the Web's social graph. Analyzing these interactions helps us to predict what people will want to do next and whether their actions will be followed. These predictions can be used for marketing or for marketing to people based on what they're interested in. 13.
9. Face2Face - Realtime Face Capture and Tracking
Face2Face allows you to capture your face using only your webcam, then overlay it onto a video of someone else talking. You can even choose to have your face be the sole foreground in the video. If that was not enough, you can also add augmented reality or virtual reality elements to the video. As it is real time tracking, Face2Face supports a lot of different poses, and you can identify people’s ages, genders, and other characteristics just by watching their face. Best of all, you can actually go live right now in Face2Face’s public beta. Price: Free Developers: Face2Face is a free open-source technology available for all, you can fork the project on GitHub and use any of the multiple libraries it is built with.
8. Visual Recognition of Shapes and Colors
Pick out the 10 best open-source visual recognition tools and scripts that are currently available online. 9. Extracting a Website from YouTube There are many platforms that provide the possibility to extract a website from YouTube. Find out which of them are the best and go with those platforms. 10. Recognizing Food from Photos If you like making interesting food videos with your blog or website, you can use the visual recognition techniques to identify the characteristics of the food that you are consuming. You can apply them in the form of a game to test your humanness and creativity! 11. Identifying Images Using Manual Focus Many of us know that using manual focus on an image can sometimes give a better result than using auto focus.
7. Design an Image Classifier
If you have ever trained an image classifier for a classifier, you will know how difficult it is to classify an image. What is the best way to classify an image? A few years back, Pachinko developer raised a really good question: How can we build a model that will classify an image? It is a very important question for image classification. One of the main challenges faced is the complexity in developing machine learning models. This is when using supervised learning for image classification becomes quite challenging. The simplest way to classify an image is to have a user label an image. However, it would be much better to have a classifier that will differentiate an image based on how the user has classified the image.
6. Automated Sentiment Analysis
Automated sentiment analysis lets you analyze text with simple APIs and AI-enabled text analyzers to glean information about the content, purpose, and sentiment of each piece of text. You can use the results to support your business goals by marketing to the right audience. “It’s a cheap, easy, and automated way to track whether or not someone is on board, and to turn that into something like a newsletter or email campaigns,” said Vance Jackson, CEO of Return Path. 7. Instant Reading Another fast and free way to start assessing the reader sentiment around your content is via an automated platform that reads your website in different scenarios, and analyses the comments and feedback with a mobile app or desktop tool,” Jackson said.
5. Generate a Story in Text
Generating text for answering questions will take a lot of time, you might want to jump straight into the questions in order to achieve it faster. But if you do that, you’ll lose the value of understanding what your customers want, because you’re only focusing on what they want, without understanding their needs and desires. Instead of using the options to help you save time, consider a different way. If you already have a topic sentence ready and you think a series of related questions would help you achieve the goal, you can start with generating a topic sentence, which could also take you a long time.
4. Natural Language Generation (NLG) for Narrative Telling
Summary Natural Language Generation (NLG) for narrative telling has been a long-neglected area of interest in NLP, despite its high potential for social impact. This is especially true of NLG research aimed at telling stories about refugees and other real-life stories that are rarely covered by mainstream media. There is plenty of high-quality evidence out there on this topic, and because of the high potential for this research to help marginalized groups, NLG for narrative telling is included in this section, alongside experiments in the social sciences and humanities, and in areas such as linguistic games, comprehension, data mining and synthesis, and web search and content generation. Below, we examine six relevant projects from various fields of NLG research.
Content generation from scratch
Extracting narrative text from sources such as news articles, blogs, or social media posts
Automating categorization processes to extract information for personalization Finding actionable information within large volumes of unstructured data These are all interesting examples of practical use cases that arise with some of the many interesting topics you’ll read in future posts, but let’s first take a look at the good, the bad, and the ugly when it comes to all of the available NLP tools. My hope is that this post serves as a thorough look at the state of the NLP industry today. Hopefully you’ll walk away with a better understanding of what’s out there, which ones make sense to us, and the role that technology like Hadoop plays in helping solve NLP challenges.
Generating personalized stories with
There are three main components of a Memetic Model. Composition: The whole of a meme, i.e. information, technology, beliefs, behaviours etc. Form: The form of a meme, ie.