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Author: Admin | 2025-04-28
[] for line in lines[1:]: temp = line.split('\t') if temp[1] == '3': # Select the Essay Set 3 response.append(temp[-1]) # Select EssayText as response score.append(int(temp[2])) # Select score1 for human scoring only else: NoneNow, let’s format the data in such a way that it consists of the necessary columns (two columns: response and score), and then review how many rows and columns the data set consists of.# Construct a dataframe ("data") which includes response and score column data = pd.DataFrame(list(zip(response, score))) data.columns = ['response', 'score'] # Print how many rows and columns of the data set consists print(data.shape)(1808, 2)The values shown above indicate that the data set consists of 1808 rows and two columns (i.e., response and score columns). Now, let’s take a look at the first ten responses.# Preview the first ten row in the data setprint(data.head(10)) response score0 China's panda and Australia's koala are two an... 11 Pandas and koalas are similar because they are... 12 Pandas in China and Koalas in Australia are si... 13 Pandas in China only eat bamboo and Koalas in ... 24 Pandas in China and koalas from Australia are ... 05 Panda's are similar to koala's because they ar... 06 Panda's are similar to Koala's by they are bot... 27 Pandas in china are similar to koalas in Austr... 18 Pandas and koalas are similar because they eat... 19 Pandas are similar to koalas due to their very... 1Each document includes a set of words contribute to the meaning in the sentence, as well as stop words (e.g., articles, prepositions, pronouns, and conjunctions) that do not add much information to the text. Since stop words are very common and yet they only provide low-level information, removing them from the text can help us highlight words that are more important for each document. In addition, the presence of stop words leads to high sparsity and high dimensionality in the data (see curse of dimensionality). Furthermore, lowercase-uppercase texts and lemmatization are other factors that may impact the vectorization of text. Therefore, before performing TF-IDF text vectorization, a preprocessing process that involves removing stop words,
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