sentimental | Sentiment analysis

by ErikGartner Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(2)Community Discussions(10)Vulnerabilities Install Support

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Sentiment analysis made easy; built on top off solid libraries.

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sentimental Key Features

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sentimental Examples and Code Snippets

Sentimental,Usage,Examples

Python

Lines of Code : 18

License : Non-SPDX (NOASSERTION)

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from sentimental import ExampleExtractor, get_data_path

e = ExampleExtractor()
e.load_labeled_words(get_data_path() + '/sv/lexicon/positive_examples.txt', 'positive')
e.load_labeled_words(get_data_path() + '/sv/lexicon/negative_examples.txt', 'negat

Sentimental,Usage

Python

Lines of Code : 1

License : Non-SPDX (NOASSERTION)

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pip install sentimental

Community Discussions

Trending Discussions on sentimental

Add new column in dataframe based on multiple column conditions

Cant seem to iterate over a column to assign sentiment values

Training Accuracy Stuck at 0.50+-

Split character vector based on length

pyspark.sql.utils.IllegalArgumentException: 'Field "features" does not exist

No attribute "str" on dataframe when creating a plot

How can I avoid <> in Haskell?

Trying to make my program deliverable to a windows 10 environment

Flair Sentimental Analysis not Not giving Neutral results

How to replace tokens if they are used together?

QUESTION

Add new column in dataframe based on multiple column conditions

Asked 2022-Mar-08 at 14:02

I have the following dataframe with sentiments:

Text Negative Neutral Positive I lost my phone. I am sad 0.8 0.15 0.05 How is your day? 0.1 0.8 0.1 Let's go out for dinner today. 0.06 0.55 0.39 I am super pissed at my friend for cancelling the party. 0.73 0.11 0.16 I am so happy I want to dance 0 0.1 0.9 I am not sure if I should laugh or just smile 0.08 0.24 0.68

This is based on the sentimental analysis I have completed. Now, each text can be tagged as any one of the 5:

Very Negative, Negative, Neutral, Positive, Very Positive.

I want to add a new column in the dataframe that analyses the sentiments and tags as per the following rule:

1. If the value of negative or positive is most dominating and >= 0.8 (80%) then mark it as very negative or very positive.

2. If the value of negative or positive is most dominating but it is >= 0.5 but less than 0.8 then just negative or positive.

3. If the value of neutral is >= 0.5 then Neutral. There is no such thing as Very Neutral.

For the above example, the result should look like below:

Text Negative Neutral Positive Sentiment I lost my phone. I am sad 0.8 0.15 0.05 Very Negative How is your day? 0.1 0.8 0.1 Neutral Let's go out for dinner today. 0.06 0.55 0.39 Neutral I am super pissed at my friend for cancelling the party. 0.73 0.11 0.16 Negative I am so happy I want to dance 0 0.1 0.9 Very Positive I am not sure if I should laugh or just smile 0.08 0.24 0.68 Positive

How can I perform this operation in dataframe. I want to then plot a graph to see the distribution of each of those 5 sentiments. That part I can do, but I am trying to get this multiple conditions working on pandas.

Any help is greatly appreciated.

...

ANSWER

Answered 2022-Mar-08 at 04:30

You can use np.select()

Source https://stackoverflow.com/questions/71389966

QUESTION

Cant seem to iterate over a column to assign sentiment values

Asked 2022-Jan-30 at 17:30

I have tried iterating over rows in my dataframe to get sentimental values. My code is:

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ANSWER

Answered 2022-Jan-30 at 17:11

I'm not able to reproduce - probably because the error is happening later down the dataframe than you've sent here.

I'm guessing the issue is that you've got some non-strings (floats, specifically) in your Comments columns. Probably you should examine them and remove them, but you can also just convert them to strings before sentiment analysis with .astype(str):

Source https://stackoverflow.com/questions/70916877

QUESTION

Training Accuracy Stuck at 0.50+-

Asked 2021-Oct-04 at 15:33

I have been working on a comparison of the CNN and RNN deep learning models for sentimental analysis.

I built the CNN following this guide: https://machinelearningmastery.com/develop-word-embedding-model-predicting-movie-review-sentiment/ , and I got an accuracy of 90+ in CNN.

However, when I tried to recreate a LSTM model, the accuracy seems to hover around 0.5+-, and doesnt seems to improve over time. I wonder what is wrong with my codes I the only thing I have done is to replace the existing CNN model with LSTM in the model.add section. I have tried to change the loss from "binary" to "categorical", and different activation function. It still doesn't resolve the issue.

CNN RESULTS LSTM RESULTS

This is my CNN model which worked fine

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ANSWER

Answered 2021-Oct-04 at 15:33

The problem is in your LSTM layer. It is not returning a sequence of the same length. You must set return_sequences=True when stacking layer so that the second layer has a three-dimensional sequence input. After adding return_sequences = True parameter in your LSTM layer, it will give you around 90% accuracy for sure.

Source https://stackoverflow.com/questions/69436647

QUESTION

Split character vector based on length

Asked 2021-Jul-28 at 14:14

I have a character vector like below.

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ANSWER

Answered 2021-Jul-27 at 19:40

Here is one way you could do it. I believe there is a better way but this solution can also be improved. For this purpose I chose to write a custom function. There also remains a problem when there is only 1 vector left whose nchar is equal to 100. That should be fixed based on your preference.

Source https://stackoverflow.com/questions/68549088

QUESTION

pyspark.sql.utils.IllegalArgumentException: 'Field "features" does not exist

Asked 2021-May-08 at 12:52

I am trying to perform topic modelling and sentimental analysis on text data over SparkNLP. I have done all the pre-processing steps on the dataset but getting an error in LDA.

Error

Program is:

...

ANSWER

Answered 2021-May-08 at 12:52

According to the documentation, LDA includes a featuresCol argument, with default value featuresCol='features', i.e. the name of the column that holds the actual features; according to your shown schema, such a column is not present in your dataframe, hence the expected error.

It is not exactly clear which column contains the features in your dataframe - get_features or get_idf_feature (they look identical in the sample you show); assuming it is get_idf_feature, you should change the LDA call to:

Source https://stackoverflow.com/questions/67203838

QUESTION

No attribute "str" on dataframe when creating a plot

Asked 2021-Apr-28 at 07:35

I filtered largest 5 tweets with max polarity after sentimental analysis.

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ANSWER

Answered 2021-Apr-28 at 07:35

Sorting based on polarity

Source https://stackoverflow.com/questions/67295568

QUESTION

How can I avoid <> in Haskell?

Asked 2021-Feb-21 at 21:38

The program below results in <> in GHC.

...Obviously. In hindsight.

It happens because walk is computing a fixed point, but there are multiple possible fixed points. When the list comprehension reaches the end of the graph-walk, it "asks" for the next element of answer; but that is exactly what it's already trying to compute. I guess I figured the program would get to the, er, end of the list, and stop.

I have to admit, I'm a bit sentimental about this nice code, and wish I could make it work.

What should I do instead?
How can I predict when "tying the knot" (referring to the value inside the expression that says how to compute the value) is a bad idea?

...

ANSWER

Answered 2021-Feb-21 at 18:28

Here's one idea of how to fix it: well, we need a termination condition, right? So let's keep enough structure to know when we should terminate. Specifically, instead of producing a stream of nodes, we'll produce a stream of frontiers, and stop when the current frontier is empty.

Source https://stackoverflow.com/questions/66304661

QUESTION

Trying to make my program deliverable to a windows 10 environment

Asked 2020-Sep-25 at 23:06

So a bit of a broad question here.

Basically, I have designed and built a program that runs on my machine, using Python. The problem is when I turn it into an exe and try to run it on another windows 10 machine, it doesn't work.

The reason is because on my machine, I have python installed, python VLC installed and also the VLC player. Is the issue that I somehow need to package these programs (dependencies? Yes, I'm a noob) into the installation wizard or?

Would love some advice on what to do here as I'm working on a sentimental project for someone and it's really frustrating that I can't get it to work lol

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ANSWER

Answered 2020-Sep-25 at 23:03

For python-vlc, you do need VLC installed. I do not know of a way to package vlc into a python exe. I would recommend looking into independent modules, that are not just python wrappers.

Edit:

You could use the sound functions from the pygame library:

Source https://stackoverflow.com/questions/64072175

QUESTION

Flair Sentimental Analysis not Not giving Neutral results

Asked 2020-Sep-12 at 16:07

I am using Flair for sentimental analysis. However, when i try to predict the label, i am not able to get a Neutral class ever. Also, the confidence of class is too unreal, i.e it is positive with probability >0.97 always or negative with such high probability. Even the very neutral words are being predicted as positive or negative with a very high probability.

...

ANSWER

Answered 2020-Sep-12 at 16:07

The issue isn't with your code, it is the way the model (behind the scenes) is trained and the way it works. The English model Flair uses is trained on certain datasets (movie and product reviews) based on the release. If you want to look at the model file, it is usually located in the .flair sub-folder in your home directory.

Basically, you are using a pre-trained model provided to give you the score. To get a different score, you could either build your own model, possibly add to the existing model or you could use a different model.

You could try the other models and see what results you get by replacing this line:

Source https://stackoverflow.com/questions/63862107

QUESTION

How to replace tokens if they are used together?

Asked 2020-Sep-01 at 22:05

I would like to do a sentimental analysis on the topic COVID-19 using python. The problem arises that entries like "positive tested" receive a positive polarity, although this statement is a negative declaration. My current code is as follows:

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ANSWER

Answered 2020-Sep-01 at 22:05

I have solved my problem as follows:

Source https://stackoverflow.com/questions/63689735

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