twitter-sentiment-analysis | performs Sentiment Analysis on Twitter | Predictive Analytics library
kandi X-RAY | twitter-sentiment-analysis Summary
kandi X-RAY | twitter-sentiment-analysis Summary
This tool is written in PHP and it performs Sentiment Analysis on Twitter messages by using the Datumbox API 1.0v. To read more about how it works, how it should be configured etc check out the original blog post:
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Top functions reviewed by kandi - BETA
- Performs a HTTP request .
- Call API method .
- Finds the sentiment of a Tweet .
- Call the web service
- Parse json reply
- Sign HMAC .
- Normalizes arguments .
- Throw an exception based on status code .
- Get a list of tweets
- Get the status .
twitter-sentiment-analysis Key Features
twitter-sentiment-analysis Examples and Code Snippets
Community Discussions
Trending Discussions on twitter-sentiment-analysis
QUESTION
I am trying to understand this code. I do not understand how if you do:
...ANSWER
Answered 2019-Dec-19 at 15:22You absolutely do want the x_validation
to be related to the y_validation
, i.e. correspond to the same rows as you had in your original dataset.
e.g. if Validation takes rows 1,3,7 from the input x, you would want rows 1, 3, 7 in both the x_validation
and y_validation
.
The idea of the train_test_split
function to divide your dataset up into a two sets of features (the x
s) and the corresponding labels (the y
s). So you want and require
QUESTION
I'm looking for a way to get a list of unique words in a column of strings in a DataFrame.
...ANSWER
Answered 2019-Nov-24 at 00:13if you have strings in column then you would have to split every sentence into list of words and then put all list in one list - you can use it sum()
for this - it should give you all words. To get unique words you can convert it to set()
- and later you can convert back to list()
But at start you would have to clean sentences to remove chars like .
, ?
, etc. I uses regex
to keep only some chars and space. Eventually you would have to convert all words into lower or upper case.
QUESTION
I'm using the Naive Bayes Classifier from nltk to perform sentiment analysis on some tweets. I'm training the data using the corpus file found here: https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed, as well as using the method there.
When creating the training set I've done it using all ~4000 tweets in the data set but I also thought I'd test with a very small amount of 30.
When testing with the entire set, it only returns 'neutral' as the labels when using the classifier on a new set of tweets but when using 30 it will only return positive, does this mean my training data is incomplete or too heavily 'weighted' with neutral entries and is the reason for my classifier only returning neutral when using ~4000 tweets in my training set?
I've included my full code below.
...ANSWER
Answered 2019-May-26 at 22:06When doing machine learning, we want to learn an algorithms that performs well on new (unseen) data. This is called generalization.
The purpose of the test set is, amongst others, to verify the generalization behavior of your classifier. If your model predicts the same labels for each test instance, than we cannot confirm that hypothesis. The test set should be representative of the conditions in which you apply it later.
As a rule of thumb, I like to think that you keep 50-25% of their data as a test set. This of course depends on the situation. 30/4000 is less than one percent.
A second point that comes to mind is that when your classifier is biased towards one class, make sure each class is represented nearly equally in the training and validation set. This prevents the classifier from 'just' learning the distribution of the whole set, instead of learning which features are relevant.
As a final note, normally we report metrics such as precision, recall and Fβ=1 to evaluate our classifier. The code in your sample seems to report something based on the global sentiment in all tweets, are you sure that is what you want? Are the tweets a representative collection?
QUESTION
I followed the tutorial here: https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed to create a twitter sentiment analyser, which uses naive bayes classifier from the nltk library as a way to classify tweets as either positive, negative or neutral but the labels it gives back are only neutral or irrelevant. I've included my code below as I'm not very experienced with any machine learning so I'd appreciate any help.
I've tried using different sets of tweets to classify, even when specifying a search keyword like 'happy' it will still return 'neutral'. I don't b
...ANSWER
Answered 2019-May-21 at 07:51Your dataset is highly imbalanced. You yourself mentioned it in one of the comment, you have 550 positive and 550 negative labelled tweets but 4000 neutral that's why it always favours the majority class. You should have equal number of utterances for all classes if possible. You also need to learn about evaluation metrics, then you'll see most probably your recall is not good. An ideal model should stand good on all evaluation metrics. To avoid overfitting some people also add a fourth 'others' class as well but for now you can skip that.
Here's something you can do to improve performance of your model, either (add more data) oversample the minority classes by adding possible similar utterances or undersample the majority class or use a combination of both. You can read about oversampling, undersampling online.
In this new datset try to have utterances of all classes in this ratio 1:1:1 if possible. Finally try other algos as well with hyperparameters tuned through grid search,random search or tpot.
edit: in your case irrelevant is the 'others' class so you now have 4 classes try to have dataset in this ratio 1:1:1:1 for each class.
QUESTION
I am trying to train a model for binary classification. It is the sentiment analysis on tweets but the model prompts an error after epoch 1. Must be the size of the input but can't figure out exactly what input could be causing the problem. Any help is greatly appreciated.
Many thanks!
I have already tried many instances of different sizes and the problem continues,
...ANSWER
Answered 2019-Apr-17 at 10:41max_words=50
...
model.add(Embedding(max_words, embedding_dim, input_length=maxlen))
QUESTION
I am trying to predict the score values from downloaded saved model from this notebook
https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis/
It contains 4 saved model namely :
- encoder.pkl
- model.h5
- model.w2v
- tokenizer.pkl
I am using model.h5 my code here is:
...ANSWER
Answered 2019-Feb-17 at 15:09One should preprocess the text before feeding into the model, following is the minimal working script(adapted from https://www.kaggle.com/paoloripamonti/twitter-sentiment-analysis/):
QUESTION
I'm new to Apache Spark and I've been doing a project related to sentiment analysis on twitter data which involves spark streaming and kafka integration. I have been following the github code (link provided below)
https://github.com/sridharswamy/Twitter-Sentiment-Analysis-Using-Spark-Streaming-And-Kafka However, in the last stage, that is during the integration of Kafka with Apache Spark, the following errors were obtained
...ANSWER
Answered 2017-Feb-12 at 07:25The example you are trying to run is desinged for running in spark 1.5. You should either download spark 1.5 or run the spark-submit
from spark 2.1.0 but with kafka package related to 2.1.0, for example:
./bin/spark-submit --packages org.apache.spark:spark-streaming-kafka-0-8_2.11:2.1.0
.
QUESTION
I am trying to build doc2vec model, using gensim + sklearn to perform sentiment analysis on short sentences, like comments, tweets, reviews etc.
I downloaded amazon product review data set, twitter sentiment analysis data set and imbd movie review data set.
Then combined these in 3 categories, positive, negative and neutral.
Next I trinaed gensim doc2vec model on the above data so I can obtain the input vectors for the classifying neural net.
And used sklearn LinearReggression model to predict on my test data, which is about 10% from each of the above three data sets.
Unfortunately the results were not good as I expected. Most of the tutorials out there seem to focus only on one specific task, 'classify amazon reviews only' or 'twitter sentiments only', I couldn't manage to find anything that is more general purpose.
Can some one share his/her thought on this?
...ANSWER
Answered 2018-Oct-16 at 21:35How good did you expect, and how good did you achieve?
Combining the three datasets may not improve overall sentiment-detection ability, if the signifiers of sentiment vary in those different domains. (Maybe, 'positive' tweets are very different in wording than product-reviews or movie-reviews. Tweets of just a few to a few dozen words are often quite different than reviews of hundreds of words.) Have you tried each separately to ensure the combination is helping?
Is your performance in line with other online reports of using roughly the same pipeline (Doc2Vec + LinearRegression) on roughly the same dataset(s), or wildly different? That will be a clue as to whether you're doing something wrong, or just have too-high expectations.
For example, the doc2vec-IMDB.ipynb
notebook bundled with gensim
tries to replicate an experiment from the original 'Paragraph Vector' paper, doing sentiment-detection on an IMDB dataset. (I'm not sure if that's the same dataset as you're using.) Are your results in the same general range as that notebook achieves?
Without seeing your code, and details of your corpus-handling & parameter choices, there could be all sorts of things wrong. Many online examples have nonsense choices. But maybe your expectations are just off.
QUESTION
I have a large csv with thousands of comments from my blog that I'd like to do sentiment analysis on using textblob and nltk.
I'm using the python script from https://wafawaheedas.gitbooks.io/twitter-sentiment-analysis-visualization-tutorial/sentiment-analysis-using-textblob.html, but modified for Python3.
...ANSWER
Answered 2018-Oct-04 at 05:55After playing around a bit, I figured out a more elegant solution for this using pandas
QUESTION
The sentiment analysis sample at https://gallery.azure.ai/Collection/Twitter-Sentiment-Analysis-Collection-1 shows use of Filter Based Feature Selection in the training experiment, which is used to generate a SelectColumnsTransform to be saved and used in the predictive experiment, alongside the trained model. The article at https://docs.microsoft.com/en-us/azure/machine-learning/studio/create-models-and-endpoints-with-powershell explains how you can programmatically train multiple models on different datasets, save those models and create then patch multiple new endpoints, so that each can be used for scoring using a different model. The same technique can also be used to create and save multiple SelectColumnsTransform outputs, for feature selection specific to a given set of training data. However, the Patch-AmlWebServiceEndpoint does not appear to allow a SelectColumnsTransform in a scoring web service to be amended to use the relevant itransform saved during training. An 'EditableResourcesNotAvailable' message is returned, along with a list of resources that can be edited which includes models but not transformations. In addition, unlike (say) ImportData, a SelectColumnsTransform does not offer any parameters that can be exposed as web service parameters.
So, how is it possible to create multiple web service endpoints programmatically that each use different SelectColumnsTransform itransform blobs, such as for a document classification service where each endpoint is based on a different set of training data?
Any information much appreciated.
...ANSWER
Answered 2018-Jun-01 at 15:11Never mind. I got rid of the SelectColumnsTransform altogether (departing from the example experiment), instead using a R script in the training experiment to save the names of the columns selected, then another R script in the predictive experiment to load those names and remove any other feature columns.
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Install twitter-sentiment-analysis
PHP requires the Visual C runtime (CRT). The Microsoft Visual C++ Redistributable for Visual Studio 2019 is suitable for all these PHP versions, see visualstudio.microsoft.com. You MUST download the x86 CRT for PHP x86 builds and the x64 CRT for PHP x64 builds. The CRT installer supports the /quiet and /norestart command-line switches, so you can also script it.
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