@inproceedings{vidigal-2026-textblob,
    title = "From {T}ext{B}lob to {LLM} Agents: Sentiment Model Selection for {B}2{B} Technical Support with {CSAT} Ground Truth",
    author = "Vidigal, Pedro",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.121/",
    pages = "1774--1782",
    ISBN = "979-8-89176-394-4",
    abstract = "We present a five-year case study of sentiment model selection for customer satisfaction (CSAT) prediction in B2B technical support. Our evaluation uses the complete population of CSAT-rated tickets from an enterprise software company: over 500 tickets comprising ${\sim}$2,500 customer comments from 100+ organizations over five years. We evaluate 17 approaches across 5 paradigms (lexicon, off-the-shelf transformers, NLI zero-shot, multi-task LLM agent, and 12 dedicated LLM agents from 6 vendor families), plus 11 fine-tuning experiments (all achieving MCC$\leq$0). Key findings: (1) a dedicated single-task LLM agent reduces neutral bias from 69{\%} to 22{\%}, improving MCC from $-$0.018 to 0.347 ($p$$<$0.001); (2) our results are consistent with the ``Alignment Tax'' (Lin et al., 2024; Wu et al., 2025) in sentiment classification: Claude Opus 4.6 exhibits 41{\%} neutral predictions and lower recall than its budget model Haiku 4.5 ($p$=0.003); (3) ${\sim}$38{\%} of dissatisfied customers are undetectable by all 12 LLMs due to administrative requests lacking emotional language; (4) Gemini 3 Flash achieves the best MCC (0.347) at $0.60/1K, over 100${\texttimes}$ cheaper than Claude Opus. We describe the three-phase production deployment and provide practitioner recommendations.$"
}