Twitter-Sentiment-Analysis | sentiment analysis on Twitter data using Python | Predictive Analytics library

 by   ujjwalkarn Python Version: Current License: No License

kandi X-RAY | Twitter-Sentiment-Analysis Summary

kandi X-RAY | Twitter-Sentiment-Analysis Summary

Twitter-Sentiment-Analysis is a Python library typically used in Analytics, Predictive Analytics applications. Twitter-Sentiment-Analysis has no bugs, it has no vulnerabilities and it has low support. However Twitter-Sentiment-Analysis build file is not available. You can download it from GitHub.

tutorial for sentiment analysis on Twitter data using Python
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              Twitter-Sentiment-Analysis has a low active ecosystem.
              It has 119 star(s) with 95 fork(s). There are 11 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 2 have been closed. On average issues are closed in 46 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Twitter-Sentiment-Analysis is current.

            kandi-Quality Quality

              Twitter-Sentiment-Analysis has 0 bugs and 0 code smells.

            kandi-Security Security

              Twitter-Sentiment-Analysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Twitter-Sentiment-Analysis code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Twitter-Sentiment-Analysis does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              Twitter-Sentiment-Analysis releases are not available. You will need to build from source code and install.
              Twitter-Sentiment-Analysis has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              Twitter-Sentiment-Analysis saves you 100 person hours of effort in developing the same functionality from scratch.
              It has 254 lines of code, 23 functions and 7 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Twitter-Sentiment-Analysis and discovered the below as its top functions. This is intended to give you an instant insight into Twitter-Sentiment-Analysis implemented functionality, and help decide if they suit your requirements.
            • main entry point
            • Creates a twitter request
            • called on_data
            • Fetch tweets from twitter .
            • Called when an error occurs .
            • Prints the prior
            • Print the number of lines in a file .
            • Shows hello and world .
            Get all kandi verified functions for this library.

            Twitter-Sentiment-Analysis Key Features

            No Key Features are available at this moment for Twitter-Sentiment-Analysis.

            Twitter-Sentiment-Analysis Examples and Code Snippets

            No Code Snippets are available at this moment for Twitter-Sentiment-Analysis.

            Community Discussions

            QUESTION

            Does scikit-learn train_test_split preserve relationships?
            Asked 2019-Dec-20 at 08:42

            I am trying to understand this code. I do not understand how if you do:

            ...

            ANSWER

            Answered 2019-Dec-19 at 15:22

            You 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 xs) and the corresponding labels (the ys). So you want and require

            Source https://stackoverflow.com/questions/59412386

            QUESTION

            How can I get unique words from a DataFrame column of strings?
            Asked 2019-Nov-24 at 00:13

            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 00:13

            if 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.

            Source https://stackoverflow.com/questions/59009359

            QUESTION

            Naive Bayes Classifier and training data
            Asked 2019-May-26 at 22:06

            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 22:06

            When 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?

            Source https://stackoverflow.com/questions/56205724

            QUESTION

            Twitter Sentiment analysis with Naive Bayes Classify only returning 'neutral' label
            Asked 2019-May-25 at 18:32

            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 07:51

            Your 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.

            Source https://stackoverflow.com/questions/56204063

            QUESTION

            Deep Learning model prompts error after first epoch
            Asked 2019-Apr-17 at 10:41

            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 10:41
            max_words=50
            ...
            model.add(Embedding(max_words, embedding_dim, input_length=maxlen))
            

            Source https://stackoverflow.com/questions/55716573

            QUESTION

            How to predict using multiple saved model?
            Asked 2019-Feb-17 at 15:09

            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 :

            1. encoder.pkl
            2. model.h5
            3. model.w2v
            4. tokenizer.pkl

            I am using model.h5 my code here is:

            ...

            ANSWER

            Answered 2019-Feb-17 at 15:09

            One 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/):

            Source https://stackoverflow.com/questions/54733601

            QUESTION

            Spark streaming and Kafka intergration
            Asked 2018-Dec-01 at 08:50

            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 07:25

            The 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.

            Source https://stackoverflow.com/questions/42184889

            QUESTION

            Data set for Doc2Vec general sentiment analysis
            Asked 2018-Oct-16 at 21:35

            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 21:35

            How 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.

            Source https://stackoverflow.com/questions/52842474

            QUESTION

            list index out of range error with TextBlob to csv
            Asked 2018-Oct-04 at 05:55

            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 05:55

            After playing around a bit, I figured out a more elegant solution for this using pandas

            Source https://stackoverflow.com/questions/52573331

            QUESTION

            Azure Machine Learning Studio SelectColumnsTransform - how to patch or set web service input parameter?
            Asked 2018-Jun-01 at 15:11

            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 15:11

            Never 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.

            Source https://stackoverflow.com/questions/50514817

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install Twitter-Sentiment-Analysis

            You can download it from GitHub.
            You can use Twitter-Sentiment-Analysis like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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