Sentiment-Analysis | Tweets Based Sentiment Analysis to Detect | Predictive Analytics library

 by   carsonluuu Python Version: Current License: No License

kandi X-RAY | Sentiment-Analysis Summary

kandi X-RAY | Sentiment-Analysis Summary

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

Sentiment analysis, the automated extraction of emotional data from text or speech (Kharde & Sonawane, 2016), has long been a favored tool for its ability to gauge consumers’ opinions and feelings en masse with minimal input from the analyst’s end. It is often performed on Twitter and Facebook data to gain insight into how the public views a particular brand or a political candidate, or to measure the trust placed into a particular financial stock (Feldman, 2013). As such, we feel that sentiment analysis would be the perfect tool for answering a sociological question such as ours. We will first gather a lot of tweets from Twitter, which can serve as a proxy to individuals’ thoughts and the general public mood(Bollen, 2011). Next, we will perform sentiment analysis on each tweet to determine whether it is happy or sad. Then, we will split the tweets up by date and tally up the number of happy and sad tweets for each day to determine how happy or sad individuals in different regions of California felt on each day in November. Finally, we will analyze the trends in the total numbers of happy and sad tweets and compare those trends with recent events to answer our question.
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            kandi-support Support

              Sentiment-Analysis has a low active ecosystem.
              It has 4 star(s) with 3 fork(s). There are 2 watchers for this library.
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              It had no major release in the last 6 months.
              Sentiment-Analysis has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Sentiment-Analysis is current.

            kandi-Quality Quality

              Sentiment-Analysis has no bugs reported.

            kandi-Security Security

              Sentiment-Analysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              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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              Sentiment-Analysis releases are not available. You will need to build from source code and install.
              Sentiment-Analysis has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Sentiment-Analysis and discovered the below as its top functions. This is intended to give you an instant insight into Sentiment-Analysis implemented functionality, and help decide if they suit your requirements.
            • Train the KNeighbors classifier .
            • Create a bow vector from a tweet text .
            • Get the WOEID of a location .
            • Get the date of the given time .
            • Import the weights from a dictionary file .
            • Get the temperature for a given weather .
            • Get data from file .
            • One - hot encoding .
            Get all kandi verified functions for this library.

            Sentiment-Analysis Key Features

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

            Sentiment-Analysis Examples and Code Snippets

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

            Community Discussions

            QUESTION

            Create a string on the output. The output is a NoneType object
            Asked 2021-May-22 at 16:25

            I am running Aspect-Based-Sentiment-Analysis. And I get the output. I want to get this output as a string. I spent several hours in googling how to refer to the output and not only to see the output when I run a code. Maybe I don't comprehend something in Python basics and need huge support on that.

            The code I am talking about is as follows:

            ...

            ANSWER

            Answered 2021-May-22 at 16:23

            Looks like the command is just printing the output to stdout instead of acutally returning an object containing the information you are looking for. You may want to try and capture stdout while running the function. The answer to this question should be helpful.

            Source https://stackoverflow.com/questions/67651699

            QUESTION

            Issue while using transformers package inside the docker image
            Asked 2021-May-09 at 19:48

            I am using transformers pipeline to perform sentiment analysis on sample texts from 6 different languages. I tested the code in my local Jupyterhub and it worked fine. But when I wrap it in a flask application and create a docker image out of it, the execution is hanging at the pipeline inference line and its taking forever to return the sentiment scores.

            • mac os catalina 10.15.7 (no GPU)
            • Python version : 3.8
            • Transformers package : 4.4.2
            • torch version : 1.6.0
            ...

            ANSWER

            Answered 2021-Apr-13 at 12:55

            Flask uses port 5000. In creating a docker image, it's important to make sure that the port is set up this way. Replace the last line with the following:

            Source https://stackoverflow.com/questions/66797173

            QUESTION

            scikit-learn LogisticRegression Classify another value
            Asked 2021-Apr-17 at 20:37

            i'm new to python and have to make a natural language processing task. Using a kaggle dataset a sentiment classify should be implemented using python. For this i'm using a dataframe and the LogisticRegression, as described in this article and everythin works fine.

            Now i want to know if it is possible to classify another string which is not in the dataset, so that i can experiment with the classifier interactively.

            Is this possible? Thank you!

            ...

            ANSWER

            Answered 2021-Apr-17 at 19:25

            You will have to manually run all the preprocessing on youur new data, than predict.

            That is:

            So first (Data Cleaning) and other functions which you've called which edit the data,
            then run the (Create a bag of words) part, and only
            Then use the fitted LR model to predict on this (preprocessed) data.

            Source https://stackoverflow.com/questions/67141970

            QUESTION

            PyTorch ValueError: Target size (torch.Size([64])) must be the same as input size (torch.Size([15]))
            Asked 2021-Apr-07 at 13:16

            I'm currently using this repo to perform NLP and learn more about CNN's using my own dataset, and I keep running into an error regarding a shape mismatch:

            ...

            ANSWER

            Answered 2021-Apr-07 at 13:16

            QUESTION

            TypeError: '<' not supported between instances of 'function' and 'str'
            Asked 2021-Feb-02 at 09:45

            I have built a sequential model with a customized f1 score metric. I pass this during the compilation of my model and then save it in *.hdf5 format. Whenever I load the model for testing purposes using the custom_objects attribute

            model = load_model('app/model/test_model.hdf5', custom_objects={'f1':f1})

            Keras throws the following error

            ...

            ANSWER

            Answered 2021-Feb-02 at 09:45

            After model.load() if you compile your model again with the custom metric then it should work.

            Therefore, after loading your model from disk using

            Source https://stackoverflow.com/questions/65549053

            QUESTION

            Usage of turicreate.text_analytics.count_words
            Asked 2021-Jan-31 at 19:29

            I am currently learning classification using turicreate and have a question regarding the word count vector.

            Using the example that I found here

            ...

            ANSWER

            Answered 2021-Jan-31 at 19:29

            thanks for the direct question. I am here after I received your email. I think the two questions that you raised are somewhat similar and can be answered through each other. Basically, your question is why do we need word count vector while conducting sentiment analysis.

            In all honesty, this is actually a long answer but I will try to make it as concise as possible. I am not aware of your level of NLP understanding at the moment but all machine learning models are only built for numerical values which means when you are working with text data, you first need to convert the text into a numerical format. This process is known as vectorization. That is essentially what we are doing here but there are many ways of achieving that. The vectorizer that is being used here is a CountVectorizer where each word in the counts dictionary is treated as a separate feature for that particular sentence. This leads to the creation of a sparse matrix which can represent m sentences with n unique words as a m x n matrix.

            The way we're going about it is that we count the number of times a word occurs a particular type of sentence (either positive or negative). It is understandable that words like terrible might have a very high count in negative sentences and almost 0 counts in positive sentences. Similarly, there will be a reverse effect for words like 'great' and 'amazing'. This is what is used in classifiers to allot weights to each word. Negative weights to words occurring popularly in negative classes and positive weights to words occurring in positive classes. This is what sentiment analysis classification is based on.

            This might be a really helpful resource. You can also read through this.

            PS: I wouldn't recommend using TuriCreate before you have either coded this from scratch to understand how it works or used scikit-learn because TuriCreate abstracts a lot of the usage and you might not understand what is happening in the background.

            Source https://stackoverflow.com/questions/65981906

            QUESTION

            HuggingFace Bert Sentiment analysis
            Asked 2021-Jan-25 at 10:02

            I am getting the following error :

            AssertionError: text input must of type str (single example), List[str] (batch or single pretokenized example) or List[List[str]] (batch of pretokenized examples)., when I run classifier(encoded). My text type is str so I am not sure what I am doing wrong. Any help is very appreciated.

            ...

            ANSWER

            Answered 2021-Jan-25 at 10:02

            The pipeline already includes the encoder. Instead of

            Source https://stackoverflow.com/questions/65881820

            QUESTION

            Giving pretokenized input to sentiment classifier
            Asked 2020-Dec-12 at 03:10

            I am using the sentiment classifier in python according to this demo.

            Is it possible to give pre-tokenized text as input to the predictor? I would like to be able to use my own custom tokenizer.

            ...

            ANSWER

            Answered 2020-Dec-12 at 03:10

            There are two AllenNLP sentiment analysis models, and they are both tightly tied to their tokenizations. The GLoVe-based one needs tokens that correspond to the pre-trained GLoVe embeddings, and similarly the RoBERTa one needs tokens (word pieces) that correspond with its pretraining. It does not really make sense to use these models with a different tokenizer.

            Source https://stackoverflow.com/questions/65154160

            QUESTION

            BertModel transformers outputs string instead of tensor
            Asked 2020-Dec-10 at 05:08

            I'm following this tutorial that codes a sentiment analysis classifier using BERT with the huggingface library and I'm having a very odd behavior. When trying the BERT model with a sample text I get a string instead of the hidden state. This is the code I'm using:

            ...

            ANSWER

            Answered 2020-Dec-04 at 04:03

            I faced the same issue while learning how to implement Bert. I noticed that using

            Source https://stackoverflow.com/questions/65132144

            QUESTION

            OSError: SavedModel file does not exist when I try to deploy my Flask application on Heroku
            Asked 2020-Nov-19 at 06:08

            My flask application works fine on my local server. When I try to deploy it on heroku it give the following error:

            2020-11-12T13:22:11.503563+00:00 app[web.1]: OSError: SavedModel file does not exist at: /Users/leylamemiguven/Desktop/sentiment/twitter_sentiment_analysis.h5/{saved_model.pbtxt|saved_model.pb}

            My keras model is saved as a .h5 file in the root directory of the project. The path is correct as I directly copied the path from vs code. I can't seem to figure out the issue because it works just fine when I run it with $ flask run Here is the model. py file

            ...

            ANSWER

            Answered 2020-Nov-19 at 06:08

            Your file path looks like it's relative to your local PC

            Source https://stackoverflow.com/questions/64805010

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

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            Install Sentiment-Analysis

            If you're just looking to use natural without your own node application, you can install via NPM like so:.

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