Datasets:

bhoomichowksey
/

consulting-sentiment-intelligence

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 48 new columns ({'word_count', 'topic_name', 'reviewer_role', 'rating_compensation', 'location', 'rating_client_exposure', 'rating_leadership', 'helpful_count', 'rating_diversity_and_inclusion', 'vader_label', 'employment_status', 'tier', 'date', 'char_count', 'quarter', 'bias_flags', 'review_title', 'season', 'tb_polarity', 'rating_work-life_balance', 'pros', 'dominant_topic', 'review_text', 'department', 'vader_compound', 'topic_confidence', 'rating_culture', 'overall_rating', 'cons', 'confidence', 'years_at_firm', 'rating_career_growth', 'token_str', 'tb_subjectivity', 'ensemble_score', 'is_clean', 'true_sentiment', 'text_clean', 'tokens', 'tb_label', 'ensemble_label', 'review_id', 'source', 'platform', 'vader_neu', 'rating_work_quality', 'vader_pos', 'vader_neg'})

This happened while the csv dataset builder was generating data using

hf://datasets/bhoomichowksey/consulting-sentiment-intelligence/employee_reviews_processed.csv (at revision 1928aaa90deb53861ea9bddc8dcfc11c067a12d9), ['hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/aspect_monthly_trend.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/employee_reviews_processed.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/firm_divergence_summary.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/monthly_timeseries.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/portal_reviews_processed.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/sentiment_forecast.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/timeseries_enriched.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/topic_distribution.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/yoy_drift.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              review_id: string
              source: string
              platform: string
              firm: string
              tier: string
              date: string
              year: int64
              month: int64
              quarter: int64
              season: string
              reviewer_role: string
              department: string
              location: string
              employment_status: string
              years_at_firm: double
              overall_rating: double
              review_title: string
              review_text: string
              pros: string
              cons: string
              helpful_count: int64
              true_sentiment: string
              rating_work-life_balance: double
              rating_compensation: double
              rating_culture: double
              rating_leadership: double
              rating_career_growth: double
              rating_diversity_and_inclusion: double
              rating_work_quality: double
              rating_client_exposure: double
              text_clean: string
              tokens: string
              token_str: string
              word_count: int64
              char_count: int64
              vader_compound: double
              vader_pos: double
              vader_neg: double
              vader_neu: double
              vader_label: string
              tb_polarity: double
              tb_subjectivity: double
              tb_label: string
              ensemble_score: double
              ensemble_label: string
              confidence: double
              aspect_work_life_balance: double
              aspect_compensation: double
              aspect_culture: double
              aspect_leadership: double
              aspect_career_growth: double
              aspect_work_quality: double
              dominant_topic: int64
              topic_name: string
              topic_confidence: double
              bias_flags: string
              is_clean: bool
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 7369
              to
              {'firm': Value('string'), 'year': Value('int64'), 'month': Value('int64'), 'aspect_work_life_balance': Value('float64'), 'aspect_compensation': Value('float64'), 'aspect_culture': Value('float64'), 'aspect_leadership': Value('float64'), 'aspect_career_growth': Value('float64'), 'aspect_work_quality': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 48 new columns ({'word_count', 'topic_name', 'reviewer_role', 'rating_compensation', 'location', 'rating_client_exposure', 'rating_leadership', 'helpful_count', 'rating_diversity_and_inclusion', 'vader_label', 'employment_status', 'tier', 'date', 'char_count', 'quarter', 'bias_flags', 'review_title', 'season', 'tb_polarity', 'rating_work-life_balance', 'pros', 'dominant_topic', 'review_text', 'department', 'vader_compound', 'topic_confidence', 'rating_culture', 'overall_rating', 'cons', 'confidence', 'years_at_firm', 'rating_career_growth', 'token_str', 'tb_subjectivity', 'ensemble_score', 'is_clean', 'true_sentiment', 'text_clean', 'tokens', 'tb_label', 'ensemble_label', 'review_id', 'source', 'platform', 'vader_neu', 'rating_work_quality', 'vader_pos', 'vader_neg'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/bhoomichowksey/consulting-sentiment-intelligence/employee_reviews_processed.csv (at revision 1928aaa90deb53861ea9bddc8dcfc11c067a12d9), ['hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/aspect_monthly_trend.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/employee_reviews_processed.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/firm_divergence_summary.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/monthly_timeseries.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/portal_reviews_processed.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/sentiment_forecast.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/timeseries_enriched.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/topic_distribution.csv', 'hf://datasets/bhoomichowksey/consulting-sentiment-intelligence@1928aaa90deb53861ea9bddc8dcfc11c067a12d9/yoy_drift.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

firm string	year int64	month int64	aspect_work_life_balance float64	aspect_compensation float64	aspect_culture float64	aspect_leadership float64	aspect_career_growth float64	aspect_work_quality float64
Accenture Strategy	2,019	1	0.0293	-0.1456	0.0201	-0.1322	0.1657	0.0835
Accenture Strategy	2,019	2	-0.0042	-0.1947	-0.065	0.1168	0.1335	0.1905
Accenture Strategy	2,019	3	-0.0889	-0.0678	0.0164	0.01	0.2648	0.0784
Accenture Strategy	2,019	4	-0.0682	0.067	0.1967	0.1882	0.3952	0.1795
Accenture Strategy	2,019	5	0.0254	0	0.1207	-0.15	0.3271	0.1391
Accenture Strategy	2,019	6	-0.1941	-0.22	-0.507	-0.2443	-0.1621	0.009
Accenture Strategy	2,019	7	0.0503	-0.3405	-0.112	0	0.0503	0.3908
Accenture Strategy	2,019	8	0.1937	-0.0831	0.1666	0.1221	0.3536	0.2775
Accenture Strategy	2,019	9	-0.048	-0.0659	-0.0628	-0.1777	0.099	0.0122
Accenture Strategy	2,019	10	-0.6148	-0.1084	-0.4084	-0.3656	0	-0.3472
Accenture Strategy	2,019	11	-0.0762	-0.0215	0.2492	0.0437	0.3621	0.4066
Accenture Strategy	2,019	12	-0.0983	-0.113	-0.0433	0.1325	0.1417	0.2311
Accenture Strategy	2,020	1	-0.1074	-0.0276	-0.0161	0.0434	0.191	0.136
Accenture Strategy	2,020	2	-0.0946	0.0909	0.4308	0.0932	0.4053	0.2438
Accenture Strategy	2,020	3	-0.2069	-0.0774	-0.2639	-0.2611	0.02	-0.0157
Accenture Strategy	2,020	4	0.0245	-0.0678	-0.0678	-0.1935	0.1622	0.0922
Accenture Strategy	2,020	5	-0.1174	-0.2166	-0.117	-0.0729	0.1746	0.1
Accenture Strategy	2,020	6	-0.0468	0.1035	0.0174	0.1304	0.1595	0.0967
Accenture Strategy	2,020	7	0.219	-0.2912	-0.2912	-0.1828	0.1298	0.3274
Accenture Strategy	2,020	8	-0.0935	0.1079	0.1326	-0.0211	0.1128	0.1271
Accenture Strategy	2,020	9	0.0357	-0.1807	0.0787	0.2593	0.4757	0.2163
Accenture Strategy	2,020	10	0.1285	-0.2427	-0.2427	-0.1523	0	0.2188
Accenture Strategy	2,020	11	-0.1502	-0.0696	-0.1259	0.0194	0.1088	-0.0807
Accenture Strategy	2,020	12	-0.3115	-0.1355	0.0875	0.1945	0.1945	0.0285
Accenture Strategy	2,021	1	0.2094	0.0953	0.349	0.1713	0.3423	0.4706
Accenture Strategy	2,021	2	-0.051	0	0.293	-0.344	0.293	-0.051
Accenture Strategy	2,021	3	0.0106	0.0337	-0.182	0.037	0.1813	0.0784
Accenture Strategy	2,021	4	-0.0363	0	0.0106	-0.0363	0.1111	-0.0363
Accenture Strategy	2,021	5	-0.1618	0.3185	0.0147	0.454	0.2382	0.212
Accenture Strategy	2,021	6	-0.0192	-0.2168	-0.0612	0.1556	0.1556	0.1976
Accenture Strategy	2,021	7	-0.0311	0.0457	-0.0027	0.135	0.1358	0.135
Accenture Strategy	2,021	8	-0.0708	-0.1298	0.0881	0.0582	0.2204	0.059
Accenture Strategy	2,021	9	0.0665	0	0	-0.258	0.3245	0.0665
Accenture Strategy	2,021	10	-0.1704	-0.3906	-0.3906	-0.2043	-0.0124	0.0896
Accenture Strategy	2,021	11	0.0682	0.4765	0.4765	0.4765	0.6768	0.5448
Accenture Strategy	2,021	12	-0.5098	-0.1084	-0.251	0.0524	0.1556	-0.4014
Accenture Strategy	2,022	1	-0.2648	0.0357	0.0391	-0.0761	0	-0.0178
Accenture Strategy	2,022	2	-0.0604	-0.1828	-0.0234	-0.2038	0.3186	0.2792
Accenture Strategy	2,022	3	-0.542	-0.542	-0.542	0	0	0
Accenture Strategy	2,022	4	-0.0621	-0.0187	-0.053	0.2473	0.1946	0.2289
Accenture Strategy	2,022	5	0.1335	0.0863	0.0336	0.0845	0.2213	0.2994
Accenture Strategy	2,022	6	0.1374	0	0.2125	-0.0318	0.3554	0.2526
Accenture Strategy	2,022	7	-0.32	-0.2168	-0.3186	-0.1186	-0.1018	-0.1032
Accenture Strategy	2,022	8	-0.1088	-0.271	-0.0553	0	0.1622	0.378
Accenture Strategy	2,022	9	0.2083	0.0535	-0.1752	0.1438	0.1315	0.2987
Accenture Strategy	2,022	10	-0.0766	0	0.34	-0.0508	0.4698	0.1078
Accenture Strategy	2,022	11	0.0186	-0.2974	-0.2683	-0.1306	0.0019	0.3087
Accenture Strategy	2,022	12	0.252	-0.1523	0.166	0.107	0.4265	0.252
Accenture Strategy	2,023	1	-0.0872	0.1588	0.2855	0.2855	0.5517	0.2043
Accenture Strategy	2,023	2	0	0.4315	-0.2545	0.4315	-0.2545	0
Accenture Strategy	2,023	3	0.0078	-0.0659	0.0337	-0.0162	0.188	0.2523
Accenture Strategy	2,023	4	0.0357	-0.1807	-0.3503	0	0.0467	0.2163
Accenture Strategy	2,023	5	-0.105	-0.0678	-0.0405	-0.0741	0.2965	0.078
Accenture Strategy	2,023	6	-0.2507	-0.1807	-0.4153	0	0	-0.07
Accenture Strategy	2,023	7	-0.0576	-0.1034	-0.1993	0.03	0.0051	0.1418
Accenture Strategy	2,023	8	0.1088	-0.0051	0.086	0.1505	0.4577	0.201
Accenture Strategy	2,023	9	-0.1173	0.4615	0.33	0.5783	0.4473	0.2003
Accenture Strategy	2,023	10	0.1242	-0.0452	0.114	0.0218	0.3928	0.2462
Accenture Strategy	2,023	11	-0.1916	-0.2336	-0.019	0	0.0576	0.2146
Accenture Strategy	2,023	12	-0.1843	-0.0774	-0.0669	-0.0363	0.1817	-0.1069
Accenture Strategy	2,024	1	0.1509	0.1322	0.2187	0.2309	0.3457	0.3263
Accenture Strategy	2,024	2	-0.0822	-0.1043	0.3228	0.0437	0.2872	0.3197
Accenture Strategy	2,024	3	0.0043	0	0.3416	0.0509	0.4343	0.136
Accenture Strategy	2,024	4	-0.258	0	0.389	0.131	0.389	-0.258
Accenture Strategy	2,024	5	-0.1916	-0.2336	-0.1916	0	0.2178	0.042
Accenture Strategy	2,024	6	0.138	0.1361	0.1746	0.1736	0.1746	0.2741
Accenture Strategy	2,024	7	-0.1802	-0.0602	-0.0503	-0.0651	0.1754	0.0701
Accenture Strategy	2,024	8	0.0303	-0.2563	-0.0013	0	0.1689	0.4183
Accenture Strategy	2,024	9	0.2286	0	0.192	0.2938	0.3218	0.2286
Accenture Strategy	2,024	10	0.1002	-0.0853	0.0585	0.005	0.2655	0.4917
Accenture Strategy	2,024	11	-0.1005	-0.2087	-0.079	0.104	0.0292	0.1082
Accenture Strategy	2,024	12	-0.0313	0.0501	0.043	0.2892	0.3318	0.3082
Bain & Company	2,019	1	0.1593	0.0307	0.2307	-0.0033	0.3613	0.5121
Bain & Company	2,019	2	-0.1506	-0.0678	0.2159	0.1169	0.362	0.1189
Bain & Company	2,019	3	-0.0999	0.2108	0.3931	0.1252	0.4583	0.2068
Bain & Company	2,019	4	0.3785	0.0065	0.4323	0.1362	0.5847	0.5373
Bain & Company	2,019	5	0.1389	0.0457	0.3464	0.1059	0.544	0.6058
Bain & Company	2,019	6	-0.388	-0.1669	0.0676	-0.11	0.0217	-0.0979
Bain & Company	2,019	7	0.0296	0.0043	0.1961	-0.1189	0.2501	0.3272
Bain & Company	2,019	8	0.1321	-0.0626	0.1284	0.1556	0.397	0.281
Bain & Company	2,019	9	0.1955	0	0.2158	-0.129	0.3245	0.4112
Bain & Company	2,019	10	0.2163	-0.3047	-0.3047	-0.3613	0.3498	0.2163
Bain & Company	2,019	11	-0.0098	-0.0235	0.2138	0.2472	0.3054	0.1509
Bain & Company	2,019	12	0.0607	0.0909	0.3264	0.351	0.4876	0.0607
Bain & Company	2,020	1	-0.0678	0.0401	0.1495	0.3791	0.3174	0
Bain & Company	2,020	2	0.494	0	0	0	0	0.494
Bain & Company	2,020	3	-0.0806	-0.0774	0.3544	0.2349	0.3086	0.1201
Bain & Company	2,020	4	-0.0865	-0.4993	-0.3697	-0.175	0.1335	0.1082
Bain & Company	2,020	5	-0.1	-0.1306	-0.1214	0.1021	0.2164	0.1546
Bain & Company	2,020	6	0.1045	0.1438	0	-0.0117	0.1082	0.1045
Bain & Company	2,020	7	0.2339	0.0056	0.24	0.1167	0.34	0.4933
Bain & Company	2,020	8	0.0827	-0.0719	-0.0719	-0.0681	0.2289	0.2963
Bain & Company	2,020	9	-0.0004	-0.0678	0.1554	-0.129	0.2775	0.2905
Bain & Company	2,020	10	-0.0553	-0.271	-0.0553	-0.0192	0.2158	0.2158
Bain & Company	2,020	11	-0.2375	-0.1565	0.038	0.0453	0.038	-0.081
Bain & Company	2,020	12	0.0252	-0.1301	-0.071	-0.1254	0.1512	0.0982
Bain & Company	2,021	1	-0.0663	-0.0659	-0.1536	0.0808	0.4256	-0.0061
Bain & Company	2,021	2	-0.067	0	0.0628	-0.086	0.3137	0.0867
Bain & Company	2,021	3	-0.3893	-0.3893	-0.082	0	0.0987	0.3073
Bain & Company	2,021	4	-0.0582	-0.1084	0.0472	0.0524	0.1556	0.0502

End of preview.

YAML Metadata Warning:The task_categories "sentiment-analysis" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

🔍 Consulting Firm Review & Sentiment Intelligence Dataset

Overview

6,200+ NLP-enriched reviews across 10 top consulting firms (MBB, Big 4, Tier 2), covering:

5,000 employee reviews — Glassdoor, AmbitionBox, Blind, Indeed, Comparably
1,200 portal reviews — Official firm client testimonials

Each review includes VADER + TextBlob ensemble sentiment, 6-dimension aspect scores, NMF topic labels, and a firm-level Credibility Divergence Index.

Files

File	Rows	Description
`employee_reviews_processed.csv`	5,000	Full NLP enrichment + aspects + topics
`portal_reviews_processed.csv`	1,200	Portal NLP scores
`firm_divergence_summary.csv`	10	Divergence Index per firm
`timeseries_enriched.csv`	719	Monthly aggregates + spike flags
`sentiment_forecast.csv`	60	6-month polynomial forecast
`yoy_drift.csv`	60	Year-over-year drift
`topic_distribution.csv`	70	NMF topic sentiment per firm
`aspect_monthly_trend.csv`	719	6-aspect monthly trends

Firms Covered

McKinsey & Company · Boston Consulting Group · Bain & Company · Deloitte Consulting · PwC Advisory · EY Consulting · KPMG Advisory · Accenture Strategy · Oliver Wyman · Roland Berger

Key Columns (employee_reviews_processed.csv)

Column	Description
`ensemble_score`	Weighted sentiment (-1 to +1)
`ensemble_label`	positive / neutral / negative
`vader_compound`	VADER score (domain-tuned)
`tb_polarity`	TextBlob polarity
`tb_subjectivity`	TextBlob subjectivity
`confidence`	Model confidence
`aspect_work_life_balance`	WLB aspect score
`aspect_compensation`	Comp aspect score
`aspect_culture`	Culture aspect score
`aspect_leadership`	Leadership aspect score
`aspect_career_growth`	Growth aspect score
`aspect_work_quality`	Work quality aspect score
`topic_name`	NMF topic label
`bias_flags`	Quality flags

Usage

from datasets import load_dataset
ds = load_dataset("bhoomichowksey/consulting-sentiment-intelligence")
emp = ds["employee_reviews_processed"]

Citation

@dataset{consulting_sentiment_2024,
  title={Consulting Firm Review & Sentiment Intelligence Dataset},
  author={Bhoomi Chowksey}, year={2024},
  publisher={Hugging Face}
}

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