Sentiment_analysis | 这个是对汽车之家口碑网某款车型的评论数据进行的情感分析，设计思路如下：
kandi X-RAY | Sentiment_analysis Summary
kandi X-RAY | Sentiment_analysis Summary
这个是对汽车之家口碑网某款车型的评论数据进行的情感分析，设计思路如下： 1）首先是编写网络爬虫，本次使用的是Scrapy框架，都知道汽车之家的网页是有很强的反爬机制的，一直在更新，不过最后获取到了数据。 2）进行数据处理，获取到口碑网评论数据后，对数据进行分词，去除停用词等操作 3）准备需要使用的情感词库，比如positive积极词等 4）编写完善代码对数据进行情感分析 5）编写产生词云的代码，方便直观看.
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QUESTION
def opinion_df_gen(preprocessor):
op_files = {'POSITIVE': Path('clustering/resources/positive_words.txt').resolve(),
'NEGATIVE': Path('clustering/resources/negative_words.txt').resolve()}
df_ = pd.DataFrame()
for i, (sentiment, filepath) in enumerate(op_files.items()):
print(filepath)
word_set = preprocessor.lemmatize_words(file_path=filepath)
print(len(word_set))
df_['tokens'] = list(word_set)
ANSWER
Answered 2021-May-10 at 11:30The problem is you are calling same column in a for loop. You have two iterations: first is for positive words and second is for negative words. When you have completed first iteration , the column df_['tokens'] has words_set of positive_words in it . This list should have been of length 4085. So, what happens next is when the second iteration came in , i.e for negative_words, the length of words_set of negative_words is 1782, which is different to the df_['tokens']. Note that it is not empty now. Hence the error
QUESTION
I am attempting to obtain sentiment scores on comments in a data frame with two columns, Author and Comment. I used the command
...ANSWER
Answered 2021-Apr-23 at 13:21Welcome to SO, Père Noël.
Pacakge {sentimenter}'s get_sentences() breaks the text input into sentences by default, as its name implies. To reconstruct the original text input as the defining key in your final data frame, you need to group and summarize the sentence-based output produced by sentiment().
In this example, I will simply average the sentiment scores, and append sentences by their element_id.
QUESTION
I'm using Python 3.6.7 and Pyspark 2.3.0 and spark 2.3.0 on a jupyter notebook to extract tweets from kafka and process them using spark streaming. On the running the following code :
...ANSWER
Answered 2020-Oct-21 at 18:33it is a question of compatibility please to refer to this link : http://spark.apache.org/docs/latest/streaming-programming-guide.html#advanced-sources So to solve your issue use kafka libraries of a version compatible with your spark version.
QUESTION
I have a python script that uses pandas and pyqt5 where the user loads a csv file in a qtableWidget and prints the records that include NaN values then the user can drop these records. in order to drop the Nan values the system takes the user input through QInputDialog.
The problem occurs when the user drops the NaN values and try to print the records the system crash and display the below error:
code: ...self.display_nan.clicked.connect(lambda: self.print_df_NaN(self.df)) File "f:\AIenv\sentiment_analysis\qt_designer\harak_anal_2_mainUI.py .py", line 189, in print_df_NaN print(self.df[df.isna().any(axis=1)]) AttributeError: 'NoneType' object has no attribute 'isna'
ANSWER
Answered 2020-Sep-10 at 10:16From the pandas documentation for DataFrame.dropna:
inplacebool, default False
If True, do operation inplace and return None.
This means that the line
QUESTION
Train on 28624 samples
Epoch 1/10
32/28624 [..............................] - ETA: 15:20
InvalidArgumentError Traceback (most recent call last)
in
----> 1 model.fit(X_train_indices, Y_train_OH, epochs = 10, batch_size = 32)**
InvalidArgumentError: indices[15,2] = -2147483648 is not in [0, 1193514)
[[node model_1/embedding_1/embedding_lookup (defined at :1) ]] [Op:__inference_distributed_function_6120]
Errors may have originated from an input operation.
Input Source operations connected to node model_1/embedding_1/embedding_lookup:
model_1/embedding_1/embedding_lookup/4992 (defined at C:\Users\shash\Anaconda3\envs\sentiment_analysis\lib\contextlib.py:81)
Function call stack:
distributed_function
ANSWER
Answered 2020-Jul-12 at 07:18The problem is when the words are being replaced by their corresponding index. If the word wasn't found in the vocabulary/word_to_index dictionary it was being stored as nan.
The vocabulary is all the words present in the word embeddings (I have used GloVe twitter embeddings).
Modified function:
QUESTION
I am trying to build a docker image of python using the following dockerfile: Could you help please to clarify.
...ANSWER
Answered 2020-May-05 at 08:24After suggesting of Stefano to add RUN apk add build-base right after the FROM directive - it works! Thanks, Stefano!
QUESTION
I have a python that read from CSV file and converts it to dataframe using pandas then using matplotlib it plots a histogram. the first task is correct it read and write to and from CSV file.
the csv file fileds are: date","user_loc","message","full_name","country","country_code","predictions","word count"
BUT the task of plotting is displaying the below error.
Error:
...ANSWER
Answered 2020-Mar-30 at 12:49It is not a dataframe, it is a numpy array. The result of your predict() method is a numpy array which cannot be indexed like you are trying to. Why not just use the dataframe that you append the prediction to, 'df_tweet_preds['predictions'] = tweet_preds'. Then you can do all sorts of indexing.
QUESTION
I have a project in Django. And i have a form named report.html. This form has got fields that users enters date ranges. The form is shown below. This is a form that allows users to select date ranges
So the project has got a model named sentiment.py this model takes sentiments from the database and analyses them the comments are filtered with date ranges . Below is the code of the file `# Main function
...ANSWER
Answered 2020-Mar-12 at 11:51This has been solved by using the django_rest_framework. You may close the question.
QUESTION
I have been trying to find a way to deploy a trained PySpark pipeline as an API, and I ended up landing on both Flask and PMML as possible solutions.
As far as I am aware, the generation of the PMML file is working: I train the pipeline using ParamGridBuilder, obtain the best model, and spit it out as a .pmml file.
A problem arises, though, when I load the resulting file into Flask. I am able to get the API running just fine; however, when I send it a request, I am not getting the expected result (the sentiment contained in the text), but the following error.
...ANSWER
Answered 2020-Mar-03 at 07:06Your arguments DataFrame contains a complex column type; The Java backend that you have chosen (PyJNIus) does not know how to map this Python value to a Java value.
Things you can try if you want to keep going down this roll-your-own Flask API way:
- Update the
jpmml_evaluatorpackage to the latest. There were new value conversion added after 0.2.3. A newer package version should tell you exactly what's the problematic column type. See the source code ofjpmml_evaluator.pyjnius.dict2mapmethod. - Choose a different Java backend. Specifically, try replacing PyJNIus with Py4J.
- Replace the complex column type with something simpler in your Python code.
All things considered, you would be much better off serving your PySpark models using the Openscoring REST web service. There is an up-to-date tutorial available about deploying Apache Spark ML pipeline models as a REST web service.
QUESTION
I need to normalize the eigenvectors for two different shape.After writing this code:
...ANSWER
Answered 2019-Dec-21 at 18:16You cannot use a StandardScaler because X given at transform should have the same number of the column than the X given at fit.
Basically, at fit, you learnt a vector of mean/std. dev. of dimension (50,) while you try to apply these statistics on your new X with only 3 columns.
So if I understand correctly how you want to normalize, you can make your own scaler class:
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Install Sentiment_analysis
You can use 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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