Trying to create a network file for Gephi but my code gives this error - "cannot unpack non-iterable NoneType object"

Question

The purpose of this code is to output node and edges file to be used in Gephi. I have used this code as base, which is for Chinese language and I am trying to re-create it to handle English language - https://pythonmana.com/2022/01/202201060336539842.html#%E5%9B%9B%E3%80%81%E5%AF%BC%E5%85%A5Gephi%20%E5%88%B6%E4%BD%9C%E7%BD%91%E7%BB%9C%E5%9B%BE

However, I am getting this error - "cannot unpack non-iterable NoneType object"

Error is coming from this part of the code which is the main function, is there something wrong with it?

if __name__ == '__main__':
     os.chdir(r'path')
     filePath1 = r'.\node.xlsx'
     filePath2 = r'.\edge.xlsx'
     df = pd.read_excel(r'path')
     df2 = [w for w in df['Translated'] if len(w)>20]
     co_ist = [" ".join(my_cut(w)) for w in df2]
     node_str, edge_str = build_matrix(co_ist, is_reverse = True)
     str2csv(filePath1,node_str,'node')
     str2csv(filePath2,edge_str,'edge')

Error results from this particular line -

node_str, edge_str = build_matrix(co_ist, is_reverse = True)

Just adding the codes before the main function(given above) here for better clarity -

def my_cut(text):
    word_dict_file = pd.read_excel(r'path')
    stop_words = []
    with open(r'path')
        lines = f.readlines()
        for line in lines:
            stop_words.append(line.strip())
        return [w for w in nltk.word_tokenize(text) if w not in stop_words and len(w)>1] #stop_words[:10]


def str2csv(filePath, s, x):
    if x=='node':
        with open(filePath, 'w', encoding='UTF-8') as f:
            f.write("Label,Weight\r")
            f.write(s)
        print('Write file successful'+ filePath + 'View in')
    else:
        with open(filePath, 'w', encoding='UTF-8') as f: 
            f.write("Source,Target,Weight\r")
            f.write(s)
        print('Write file successful'+ filePath + 'View in')

#Build dictionary
def sortDictValue(dict, is_reverse): 
    tups = sorted(dict.items(), key=lambda item: item[1], reverse=is_reverse)
    s = ''
    for tup in tups: # Merge into csv Comma delimited format required
        s = s + tup[0] + ',' + str(tup[1]) + '\n'
    return s

#construct co-occurence matrix 
def build_matrix(co_authors_list, is_reverse): 
    node_dict = {} 
    edge_dict = {}
    row_authors_list = []
    connect_list = []
    global row_authors
    for row_authors in co_authors_list:
        row_authors_list = row_authors.split(' ')
    for index, pre_au in enumerate(row_authors_list):
        if pre_au not in node_dict:
            node_dict[pre_au] = 1
        else:
            node_dict[pre_au] += 1
        if pre_au == row_authors_list[-1]:
            break
        connect_list = row_authors_list[index+1:]
    for next_au in connect_list:
        A, B = pre_au, next_au
        if A==B:
            continue
        if A > B:
            A, B = B, A
            key = A+','+B
        if key not in edge_dict:
            edge_dict[key] = 1
        else:
            edge_dict[key] += 1
            node_str = sortDictValue(node_dict, is_reverse)
            edge_str = sortDictValue(edge_dict, is_reverse)
        return node_str, edge_str