How Emotion Analysis Boosts Intelligent Customer Service: Models, Experiments, and Insights

Introduction

Human‑machine dialogue is a key research direction in natural language processing. Recent advances have led to intelligent customer‑service systems that combine robot and human agents to improve efficiency and quality. Major enterprises such as Fujitsu, JD.com and Alibaba have built their own platforms.

Emotion Analysis Architecture in Intelligent Customer Service

Figure 1 shows a classic human‑machine hybrid architecture where users can switch between robot and human agents. Emotion analysis is applied at multiple layers to enhance the system’s human‑like abilities.

1 User Emotion Detection

We propose an integrated model that combines word‑level, phrase‑level and sentence‑level semantic features to classify emotions such as “anxious”, “angry” and “thankful”. The architecture is illustrated in Figure 2.

Sentence‑level features are extracted with the SWEM model, which pools word embeddings and achieves performance comparable to CNN and RNN baselines.

Phrase‑level features are obtained by applying CNN kernels of sizes 2, 3, 4 with 16 filters each.

Word‑level features are derived from the LEAM model, which jointly embeds words and label vectors to strengthen word‑label interactions (Figure 3).

The concatenated multi‑level features are fed to a logistic‑regression classifier for final emotion prediction.

2 User Emotion Comfort

The comfort framework consists of offline and online parts (Figure 4). Offline, seven common negative emotions are identified and a knowledge base of 35 topic expressions is built. Expert‑crafted response scripts are linked to each “problem‑reply” pair.

Online, three response strategies are used: knowledge‑based matching, emotion‑and‑topic‑aware replies, and emotion‑only replies (Figure 5).

3 Emotion‑Driven Conversational Generation

An attention‑based emotional & topical Seq2Seq model encodes the source utterance into a semantic vector C, then decodes it conditioned on emotion vector E and topic vector T (Figure 6). Attention mechanisms are added to alleviate long‑range dependency loss.

Experimental results show a 72 % answer‑qualification rate and an average reply length of 8.8 Chinese characters, meeting the target 5‑20 character range.

4 Service Quality Inspection

We define two quality issues – “negative” and “poor attitude” – and build GRU‑based context models (Figure 8 and Figure 9). Model 1, which emphasizes the target utterance, outperforms Model 2.

System‑level evaluation (Table 8) demonstrates large gains in inspection efficiency and recall compared with manual review.

5 Conversation Satisfaction Estimation

We propose a satisfaction prediction model that fuses semantic features (last four user turns), emotion embeddings and answer‑source embeddings. Both classification and regression variants are tested; the regression model reduces error to a 0.007 mean difference (Figure 11, Table 9).

Conclusion

Emotion analysis has been integrated into many stages of intelligent customer‑service dialogue, yet it remains an early attempt. Further research is needed to fully realize human‑like capabilities.