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https://github.com/poltextlab/sentiment_hun


https://github.com/poltextlab/sentiment_hun

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# sentiment_hun

# Possibilities and limitations of a lexicon-based sentiment analysis of Hungarian political news 



Draft version



## Features



- Word Embedding with Word2vec model and parameters presented in the draft

- Sentiment dictionary: finds sentiment values based on given dictonary and corpus with the methods presented in the draft



## Usage



### Word Embedding



Run Word_embedding_w2v.py!

- Give the path of your folder containing all excel files of the embedding corpus!

- Give the column of the excels containing the text to embed on - NOTE: the name of this column must be identical in each excel!

- The Word2vec model is initialised with the parameters given in the draft!

- You have two options: 

	- One: Embedding of a list of positive and negative words - the result is an excel, containing all your embeddings

	- Two: Embedding of a single word and output a .txt



### Sentiment dictionary



Requirements: magyarlanc

- Download ML_folder.jar from: [https://drive.google.com/file/d/1pPIldj6nTUbNk3HmCr_9XJn0WSHwMwdZ/view?usp=sharing]

- Place ML_folder.jar into the output folder! 



Run MAIN_sentiment_dictionary.py!

- Input the excel name to analyse!

- Input the name of the column containing ids for the articles or a given text. Each row in the excel must have a unique id!

- Input the content column! The column composed of the main textual part of each excel row.

- Input the location of the dictonaries! Input the exact path where your dictionaries are located!

- Input the positive dictonary! The name of you .txt dictonary of positive words, each written seperately in a new line!

- Input the negative dictonary! The name of you .txt dictonary of negative words, each written seperately in a new line!

- You have two four ways to analyise:

	'One: Simple' = After preprocessing use brute-force search to find words in positive and negative dictonaries. Each

			token accounts for +1 or -1 respectively.



'Two': Simple with the addition of applying the "hungarian_2" stoplist 



'Three: Sentiment-score' = Use sentiment scoring after search.

 -  sentiment_value: The result of the brute-force method search

 -  ossz_sentiment = sum of all words with sentiment values

-  sentiment_threshold: ossz_sentiment / count of all tokens in an entry

-  sentiment_nullify: The ratio between negative and positive words in an entry

if sentiment_value < 0 and (sentiment_threshold > 0.1 or sentiment_nullify < 0.95 --> negative

if sentiment_value > 0 and (sentiment_threshold > 0.1 or sentiment_nullify < 0.95 --> postitive

if sentiment_threshold < 0.1 or sentiment_nullify > 0.95 --> neutral



'Four': Sentiment-score with the addition of applying the "hungarian_2" stoplist



The output is an excel file named "sentiment.xlsx" in the output folder along with a brief overview of choice of sentiment for each row of the desired excel.



The packages used in both programs belong to their rightful owners!

## Dependencies and credits:

 * [Gensim's Word2Vec] developed by Mikolow et al.

 * [pandas]

 * [magyarlanc]

 * [xlwt 1.3.0]

 * [NLTK's hungarian stoplist] (we use a modified version)

 



[Gensim's Word2Vec]: 

[pandas]: 

[magyarlanc]: 

[xlwt 1.3.0]: 

[NLTK's hungarian stoplist]: 

[https://drive.google.com/file/d/1pPIldj6nTUbNk3HmCr_9XJn0WSHwMwdZ/view?usp=sharing]: 



Orsolya Ring, Martina Katalin Szabó, Csenge Guba, Bendegúz Váradi, István Üveges: Approaches to Sentiment Analysis of Hungarian Political News at Sentence Level with Dictionary-based Method and with Machine Learning (Under review)



 The research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program.