I am developing an application for analyizing the sentiment of the movie reviews (good/bad/neutral). The frontend will be C# dot net and backend will be MySQL.
The algorithm I am using is Genetic Algorithm and I am only directed by my guide that I need to use Rank Selection method.
I'm looking at how to approach this and if there are any existing source codes which I can refer.
Thanks in advance!
The best way is to use external API for sentiment analysis for example text2data.org
There is also free option
nltk.org
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I want to make a program that can classify news into real or fake news. In this project I've used Naive Bayes Algorithm to solve it. I have categorized fake news with sentimental analysis and I already know how to classify it.
But now I want to add constraint in this project, which is I want to have punctuation amount to decide if its a real or fake news but I am confused how to mix it. In the like-hood or what?
Thank you. Before it I have already used n-gram and laplace smoothing in the sentimental analysis of the news.
I am exploring tensorflow and would like to do sentiment analysis using the options available. I had a look at the following tutorial http://www.tensorflow.org/tutorials/recurrent/index.html#language_modeling
I have worked woth Naive Bayes Classifier, Maximum Entropy Algorithm and Scikit Learn Classifier and would like to know if there are any better algorithms offered by tensorflow. Is this the right place to start or are there any other options?
Any help pointing in the right direction would be greatly appreciated.
Thanks in advance.
A commonly used approach would be using a Convolutional Neural Network (CNN) to do sentiment analysis. You can find a great explanation/tutorial in this WildML blogpost. The accompanying TensorFlow code can be found here.
Another approach would be using an LSTM (or related network), you can find example implementations online, a good starting point is this blogpost.
I would suggest you try a character-level LSTM, it's been shown to be able to achieve state-of-the-art results in many text classification tasks one of them being sentiment analysis.
I wrote a pretty lengthy article that you can find here where I go through it's implementation in TensorFlow line by line. The result is a model that is less than 100mb in size and that achieves an accuracy of over 80% on a test set of 80,000 tweets.
Another approach that has proven to be very effective is to use a recursive neural network, you can read the paper from Stanford NLP Group here
For me, the easiest tutorial to follow was: https://pythonprogramming.net/data-size-example-tensorflow-deep-learning-tutorial/?completed=/train-test-tensorflow-deep-learning-tutorial/
It walks you throughTensorFlow.train.AdamOptimizer().minimize(cost) and uses Sentiment140 dataset (from Stanford, ~1 mil examples of positive and negative sentiment)
We are looking at building recommender system for our brand-new Learning Management System. There are a bunch of users and items (learning modules) onboarded, but no ratings yet - typical cold start problem.
To begin with, we are thinking of using a simple item-based similarity using item attributes (tags, category, etc.) The idea is to switch to more robust collaborative filtering as the ratings start coming in.
Questions:
Is this a good approach? Is there a recommended ML pattern to handle such cold-start conditions?
To realise item-based similarity, which is the right algorithm? Say, cosine similarity. However, please note there is no "matrix". Should we try to use a standard ML algorithm or maybe roll our own?
Your approach is good. I would start with an unsupervised learning algorithm such as 'k-Nearest Neighbors classifier'. If your team doesn't know the first thing about ML, I recommend you to read this tutorial http://www.astroml.org/sklearn_tutorial/general_concepts.html . It uses python and a great library called scikit-learn. From there you could do Andrew's NG course (https://www.coursera.org/learn/machine-learning/) although it does not cover any recommendation systems.
I usually go with a Pearson Correlation algorithm (https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) and that suffices me for my problems. The problem with this approach is that it is linear. I have read that the Orange data mining tool provides many correlation measures. Using it you could find which one is best for your data. I would advice against using your own algorithm.
There is an older question which provides further information on the matter: How can I implement a recommendation engine?
I am looking for an algorithm to replicate or perhaps improve upon a facebook-esque content ranking algorithm. To that end, each individual user's feed should be tailored to him/her through the parameters of affinity, time decay, and content type. What are some good resources / launchpads to reference in order to implement an algorithm for a facebook style personalized content feed?
That is not a trivial project. That said, bishop is my go-to machine learning book, and the stanford book is similarly for information retrieval.
I'd like you to give me some advice in order to tackle this problem. At college I've been solving opinion mining tasks but with Twitter the approach is quite different. For example, I used an ensemble learning approach to classify users opinions about a certain Hotel in Spain. Of course, I was given a training set with positive and negative opinions and then I tested with the test set. But now, with twitter, I've found this kind of categorization very difficult.
Do I need to have a training set? and if the answer to this question is positive, don't you think twitter is so temporal so if I have that set, my performance on future topics will be very poor?
I was thinking in getting a dictionary (mainly adjectives) and cross my tweets with it and obtain a term-document matrix but I have no class assigned to any twitter. Also, positive adjectives and negative adjectives could vary depending on the topic and time. So, how to deal with this?
How to deal with the problem of languages? For instance, I'd like to study tweets written in English and those in Spanish, but separately.
Which programming languages do you suggest to do something like this? I've been trying with R packages like tm, twitteR.
Sure, I think the way sentiment is used will stay constant for a few months. worst case you relabel and retrain. Unsupervised learning has a shitty track record for industrial applications in my experience.
You'll need some emotion/adj dictionary for sentiment stuff- there are some datasets out there but I forget where they are. I may have answered previous questions with better info.
Just do English tweets, it's fairly easy to build a language classifier, but you want to start small, so take it easy on yourself
Python (NLTK) if you want to do it easily in a small amount of code. Java has good NLP stuff, but Python and it's libraries are way more user friendly
This site: https://sites.google.com/site/miningtwitter/questions/sentiment provides 3 ways to do sentiment analysis using R.
The twitter package is now updated to work with the new twitter API. I'd you download the source version of the package to avoid getting duplicated tweets.
I'm working on a spanish dictionary for opinion mining, and would publish somewhere accesible.
cheers!
Sentiment Analysis will give only 3 results as said above - positive, negative and neutral. I found a tutorial on Twitter Sentiment analysis and it's quiet easy.
I found it here - https://www.ai-ml.tech/twitter-sentiment-analysis/
Only 3 dependencies, i downloaded and lesser code, done. Just go through it, you will get the solution.