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| import pandas as pd | |
| from datasets import load_dataset | |
| import streamlit as st | |
| from clarin_datasets.dataset_to_show import DatasetToShow | |
| class AspectEmoDataset(DatasetToShow): | |
| def __init__(self): | |
| self.dataset_name = "clarin-pl/aspectemo" | |
| self.subsets = ["train", "test"] | |
| self.description = """ | |
| Description AspectEmo Corpus is an extended version of a publicly available PolEmo 2.0 | |
| corpus of Polish customer reviews used in many projects on the use of different methods in sentiment | |
| analysis. The AspectEmo corpus consists of four subcorpora, each containing online customer reviews from the | |
| following domains: school, medicine, hotels, and products. All documents are annotated at the aspect level | |
| with six sentiment categories: strong negative (minus_m), weak negative (minus_s), neutral (zero), | |
| weak positive (plus_s), strong positive (plus_m). | |
| Tasks (input, output and metrics) | |
| Aspect-based sentiment analysis (ABSA) is a text analysis method that | |
| categorizes data by aspects and identifies the sentiment assigned to each aspect. It is the sequence tagging | |
| task. | |
| Input ('tokens' column): sequence of tokens | |
| Output ('labels' column): sequence of predicted tokens’ classes ("O" + 6 possible classes: strong negative ( | |
| a_minus_m), weak negative (a_minus_s), neutral (a_zero), weak positive (a_plus_s), strong positive ( | |
| a_plus_m), ambiguous (a_amb) ) | |
| Domain: school, medicine, hotels and products | |
| Measurements: | |
| Example: ['Dużo', 'wymaga', ',', 'ale', 'bardzo', 'uczciwy', 'i', 'przyjazny', 'studentom', '.', 'Warto', 'chodzić', | |
| 'na', 'konsultacje', '.', 'Docenia', 'postępy', 'i', 'zaangażowanie', '.', 'Polecam', '.'] → ['O', 'a_plus_s', 'O', | |
| 'O', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'a_zero', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O'] | |
| """ | |
| def load_data(self): | |
| raw_dataset = load_dataset(self.dataset_name) | |
| self.data_dict = { | |
| subset: raw_dataset[subset].to_pandas() for subset in self.subsets | |
| } | |
| def show_dataset(self): | |
| header = st.container() | |
| description = st.container() | |
| dataframe_head = st.container() | |
| with header: | |
| st.title(self.dataset_name) | |
| with description: | |
| st.header("Dataset description") | |
| st.write(self.description) | |
| full_dataframe = pd.concat(self.data_dict.values(), axis="rows") | |
| with dataframe_head: | |
| df_to_show = full_dataframe.head(10) | |
| st.header("First 10 observations of the dataset") | |
| st.dataframe(df_to_show) | |
| st.text_area(label="Latex code", value=df_to_show.style.to_latex()) | |