perform empath on text.py

if __name__ == "__main__":
    lexicon = Empath()
    result = lexicon.analyze("the quick brown fox jumps over the lazy dog", normalize=True)
    df0 = pd.Series(result, name = 'KeyValue')
    
    logging.getLogger().setLevel(logging.INFO)
    
    col_names = df0.keys()  
    df = pd.DataFrame(columns=col_names)
    
    for folder in source_folder_path_list:
        txt_list = []
        for file in os.listdir(folder):
            if file.endswith(".txt"):
                txt_list.append(file)
                
                for txt_i in txt_list:
                    txt_file_name = txt_i
        
                    #logging_str = "Coverting " + txt_i
                    #logging.info(logging_str)
        
                    txt_full_path = os.path.join(folder, txt_file_name)
                    try:
                        txt_file = open(txt_full_path, 'r')
                        lines = txt_file.readlines()
        
                        lexicon = Empath()
                        result = lexicon.analyze(lines, normalize=True)
        
                        new_result = pd.Series(result, name = txt_full_path)
                        new_result.index.name = 'Key'
                        new_result.reset_index()
        
                        df = df.append(new_result)
                        
                        logging.info(txt_i, " succesfully analyzed")
                    except:
                        logging.info(txt_i + " open failed")
                   
    df = df.dropna()
    
    # Clean the data frame
    df['Details'] = df.index
    df['Reviewer'] = df['Details'].str.split("/").str[11]
    df['Text file'] = df['Details'].str.split("/").str[12]
    df = df.set_index(['Reviewer', 'Text file'])
    df = df.drop(['Details'], axis = 1)
    
    
    df.to_csv('./data/output/Empath-on-movie-reviews_results.csv', sep=',', encoding='utf-8')