sentiment | CCF大数据比赛,基于主题的文本情感分析
kandi X-RAY | sentiment Summary
kandi X-RAY | sentiment Summary
CCF大数据比赛,基于主题的文本情感分析
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sentiment Key Features
sentiment Examples and Code Snippets
Community Discussions
Trending Discussions on sentiment
QUESTION
I am doing sentiment analysis, and I was wondering how to show the other sentiment scores from classifying my sentence: "Tesla's stock just increased by 20%."
I have three sentiments: positive, negative and neutral.
This is my code, which contains the sentence I want to classify:
...ANSWER
Answered 2021-Jun-15 at 14:44Because HappyTransformer does not support multi class probabilities I suggest to use another library. The library flair
provides even more functionality and can give you your desired multi class probabilities, with something like this:
QUESTION
Good day fam. I have this sentiment analysis API from Paysify, which returns a JSON output but all of a sudden it is now returning NULL
as output.
Please I help here, I have been trying to fix this for the past 4 hours, no headway.
ANSWER
Answered 2021-Jun-13 at 13:53This issue is not related to your code. The service you use has an issue on their server. If you try to open your Chrome browser and navigate to https://api.paysify.com/sentiment?api_key={key}&string=I+love+this+product you will see that the page can not be opened for an SSL error.
Please contact your API provider to resolve.
PS: remove the API Key from your initial question or anyone can use it.
QUESTION
I want to force the Huggingface transformer (BERT) to make use of CUDA.
nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0
or .to(cuda:0)
.
The code below is basically a customized part from german sentiment BERT working example
...ANSWER
Answered 2021-Jun-12 at 16:19You can make the entire class inherit torch.nn.Module
like so:
QUESTION
I am trying to perform a sentiment analysis based on product reviews collected from various websites. I've been able to follow along with the below article until it gets to the model coefficient visualization step.
When I run my program, I get the following error:
...ANSWER
Answered 2021-Jun-09 at 19:25You've defined feature_names
in terms of the features from a CountVectorizer
with the default stop_words=None
, but your model in the last bit of code is using a TfidfVectorizer
with stop_words='english'
. Use instead
QUESTION
I'm running several machine learning models to find the one which the highest accuracy score, however, all the accuracy scores are the exact same. I performed NLP on social media text and I'm training my models to tag sentiment based on sentiment determined from NLTK.
I'm using the same training and test sets, but I've done this method before in the past and received different scores on different models. Why are all of mine the same? Am I overfitting perhaps?
Here is my code where I'm splitting and training:
...ANSWER
Answered 2021-Jun-08 at 00:47I'm not sure what is the cause of the problem, but since the output of you SVM model and DecisionTreeClassfier always output 1, I suggest you try a more complex model like RandomForestClassifier and see what it comes out.
I've similar experience before, no matter how I tuned the training hyperparameters, the model always give the same performance metric -- this may cause by 2 probabilities:
- Our data is not suitable for the model, for example all values in the vector is zero: [0, 0, 0, 0, 0, 0, 0]
- Our model is too simple, which could only perform linear modeling, so that it could not learn too complex mapping function.
Since your SVM is built with linear kernel, could you try an more complex model and see what it comes out? And could you examine that if your X_train_vectors is all zero's in the matrix?
QUESTION
I'm just starting to learn python and I'm having problem with displaying two widget at the same time. I was trying to create a Text area where I will put a string then submit this string.
This string will be use in SentimentIntensityAnalyzer
to check the sentiment.
My goal now is to display the two widget using ipywidgets
below is my code
ANSWER
Answered 2021-Jun-07 at 16:52I used IPython.display
again, my wrong way is that I put the both variables inside the display()
Here's my code
QUESTION
I am currently trying to setup a gcloud appspot domain. I am consistently getting this error when I attempt to open up my appspot project on the browser:
Error: Not Found The requested URL / was not found on this server.
Here is my code. How can I fix this?
app.yaml -
...ANSWER
Answered 2021-Jun-07 at 00:51What you're trying to deploy on App Engine is a web app. You cannot deploy Android apps on App Engine. If you look at the picture in the Final System section, the system has two main parts and that is your Android app (client) and a request server. You are deploying the request server in App Engine Flex (which is a managed Compute Engine VM under the hood).
From what I can understand, your request server is designed to handle backend API calls. If you want to display some sort of UI interface when users access /
, then register the URL to a function that renders your view page.
I suggest that you study how Flask app routing works. You get that error because the URL (/
) is not associated to any of your functions on your app. You can get started at Flask docs. Here's a sample from GitHub
QUESTION
I have been executing a sentiment analysis, and have returned the positive and negative outcomes to my pd.DataFrame in the following manner.
author Text Sentiment 12323 this is text (0.25, 0.35)However, when I want to split the sentiment column (which consists of Polarity and Subjectivity), I get the following error:
ValueError: Columns must be same length as key
I have tried multiple approaches:
- str.split
- str.extract
- rounding
With the rounding approach I get an error mentioning the float NaN's could not be multiplied. So I suppose that there is a NaN in there somewhere. However, when I look for NaN's I get this answer:
...ANSWER
Answered 2021-Jun-06 at 20:52Edit: changed case from df['sentiment']
to df['Sentiment']
The string
methods won't work because it's not a string but a set
stored in the cell.
You can do this to create a new column:
QUESTION
I am learning about text mining and rTweet and I am currently brainstorming on the easiest way to clean text obtained from tweets. I have been using the method recommended on this link to remove URLs, remove anything other than English letters or space, remove stopwords, remove extra whitespace, remove numbers, remove punctuations.
This method uses both gsub and tm_map() and I was wondering if it was possible to stream line the cleaning process using stringr to simply add them to a cleaning pipe line. I saw an answer in the site that recommended the following function but for some reason I am unable to run it.
...ANSWER
Answered 2021-Jun-05 at 02:52To answer your primary question, the clean_tweets()
function is not working in the line "Clean <- tweets %>% clean_tweets
" presumably because you are feeding it a dataframe. However, the function's internals (i.e., the str_
functions) require character vectors (strings).
I say "presumably" here because I'm not sure what your tweets
object looks like, so I can't be sure. However, at least on your test data, the following solves the problem.
QUESTION
I'm using Plotly to plot some sentiment data yet I have the issue that one character has much more lines than the other so it seems that we cannot compare the two. I'm unsure how to describe for this to find the solution so I hoped that perhaps someone will understand what I'm referring to. Here are images:
We cannot really compare orange's negativity with blue's negativity, or can we? Should we have the orange have 600 as the label, it being much smaller than blue or is this correctly scaled?
I'd like this so we can say that for example, blue is less positive than orange.
I'm sorry for any confusion. Thank you.
...ANSWER
Answered 2021-Jun-03 at 12:18I don't know which objects you are manipulating, but I read pandas in your tags so I assumed it was a DataFrame.
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Install sentiment
You can use sentiment 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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